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Get A Skill, Get A Job, Get Ahead (G3) is a state-funded last-dollar scholarship program for students enrolling in eligible associate degree, certificate, and noncredit occupational training programs in the Virginia Community College System (VCCS) in five high-demand fields: early childhood education, healthcare, information technology, public safety, and skilled trades (construction and manufacturing). Launched in the 2021-22 academic year, the G3 initiative aims to increase enrollment, persistence, completion, and labor market success for low-to-middle-income learners. G3 has evolved over the past two years along with VCCS priorities to include a specific focus on boosting college enrollment and success of adult, Black and Hispanic, and low-income learners. In this brief, the authors highlight early insights from a study of G3 implementation and outcomes. Early analysis by VCCS shows that colleges are making progress in increasing enrollment in the five high-demand fields listed above. The authors draw on published enrollment and outcome data from VCCS and interview data collected from eight of the 23 community colleges implementing the G3 program to examine college-level G3 outreach, recruitment, and enrollment strategies and the process for awarding G3 aid.

As states reckon with racial enrollment and attainment gaps in higher education, some have made efforts to address them through their higher education funding. Two common approaches are outcomes-based funding (OBF) and Promise programs, as each can be designed with an explicit race equity lens. This report summarizes a two-year study examining the degree to which these race-specific state funding policies have advanced racial equity in higher education. Exploring initiatives in Minnesota, New Jersey, and North Carolina, this study examines how these policies aim to dismantle racial inequities by directing resources toward Black, Latinx, and Indigenous students and the institutions that serve them. This study draws on 25 interviews with state policymakers and institutional leaders at a purposive sample of public four-year institutions. Researchers sought the perspectives of individuals at Minority-serving Institutions (MSIs), Historically Black Colleges and Universities (HBCUs), Native American-serving non-tribal institutions (NASNTIs), and Hispanic-serving Institutions (HSIs), but also selected one Predominantly White Institution (PWI) in OBF states to understand how, if at all, the funding model incentivized institutional change. The research suggests that while policymakers express concerns that the amount of funding tied to race equity metrics is insufficient to motivate change, institutional leaders perceive that the metrics have steered funds toward under-resourced students and the institutions serving them. However, the report highlights that without addressing historical underfunding of MSIs and HBCUs, these institutions cannot compete equitably for funds under zero-sum OBF models. The report also suggests that the design of the formula, unfunded mandates, and competing state initiatives can undermine the impact of a race-explicit OBF. Race-explicit Promise programs in this study were perceived to have bolstered enrollment and retention at participating MSIs. However, because programs were designed to cover only tuition and fees, respondents suggested that they were not providing an additional benefit beyond existing federal and state aid programs. Without covering the additional costs of college, including room and board, these programs fell short of making a four-year residential college experience accessible to students for whom they were designed. The recommendations emerging from the research urge policymakers to consider the full ecosystem of postsecondary institutions when designing funding initiatives, ensuring that funding programs are additive rather than overlapping to maximize outcomes. The report also emphasizes the importance of supporting additional costs of college in Promise programs to promote equity for students experiencing systemic barriers.

This study introduces AGGA, a dataset comprising 80 academic guidelines for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in academic settings, meticulously collected from official university websites. The dataset contains 188,674 words and serves as a valuable resource for natural language processing tasks commonly applied in requirements engineering, such as model synthesis, abstraction identification, and document structure assessment. Additionally, AGGA can be further annotated to function as a benchmark for various tasks, including ambiguity detection, requirements categorization, and the identification of equivalent requirements. Our methodologically rigorous approach ensured a thorough examination, with a selection of universities that represent a diverse range of global institutions, including top-ranked universities across six continents. The dataset captures perspectives from a variety of academic fields, including humanities, technology, and both public and private institutions, offering a broad spectrum of insights into the integration of GAIs and LLMs in academia., Comment: arXiv admin note: text overlap with arXiv:2406.18842, arXiv:2501.00959

This paper introduces IGGA, a dataset of 160 industry guidelines and policy statements for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in industry and workplace settings, collected from official company websites, and trustworthy news sources. The dataset contains 104,565 words and serves as a valuable resource for natural language processing tasks commonly applied in requirements engineering, such as model synthesis, abstraction identification, and document structure assessment. Additionally, IGGA can be further annotated to function as a benchmark for various tasks, including ambiguity detection, requirements categorization, and the identification of equivalent requirements. Our methodologically rigorous approach ensured a thorough examination, with a selection of reputable and influential companies that represent a diverse range of global institutions across six continents. The dataset captures perspectives from fourteen industry sectors, including technology, finance, and both public and private institutions, offering a broad spectrum of insights into the integration of GAIs and LLMs in industry.

The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. This study conducts a text-based analysis of 160 guidelines and policy statements across fourteen industrial sectors, utilizing systematic methods and text-mining techniques to evaluate the governance of these technologies. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.

We present OLMo 2, the next generation of our fully open language models. OLMo 2 includes dense autoregressive models with improved architecture and training recipe, pretraining data mixtures, and instruction tuning recipes. Our modified model architecture and training recipe achieve both better training stability and improved per-token efficiency. Our updated pretraining data mixture introduces a new, specialized data mix called Dolmino Mix 1124, which significantly improves model capabilities across many downstream task benchmarks when introduced via late-stage curriculum training (i.e. specialized data during the annealing phase of pretraining). Finally, we incorporate best practices from T\"ulu 3 to develop OLMo 2-Instruct, focusing on permissive data and extending our final-stage reinforcement learning with verifiable rewards (RLVR). Our OLMo 2 base models sit at the Pareto frontier of performance to compute, often matching or outperforming open-weight only models like Llama 3.1 and Qwen 2.5 while using fewer FLOPs and with fully transparent training data, code, and recipe. Our fully open OLMo 2-Instruct models are competitive with or surpassing open-weight only models of comparable size, including Qwen 2.5, Llama 3.1 and Gemma 2. We release all OLMo 2 artifacts openly -- models at 7B and 13B scales, both pretrained and post-trained, including their full training data, training code and recipes, training logs and thousands of intermediate checkpoints. The final instruction model is available on the Ai2 Playground as a free research demo., Comment: Model demo available at playground.allenai.org

This letter presents the measurement of the energy-dependent neutrino-nucleon cross section in tungsten and the differential flux of muon neutrinos and anti-neutrinos. The analysis is performed using proton-proton collision data at a center-of-mass energy of $13.6 \, {\rm TeV}$ and corresponding to an integrated luminosity of $(65.6 \pm 1.4) \, \mathrm{fb^{-1}}$. Using the active electronic components of the FASER detector, $338.1 \pm 21.0$ charged current muon neutrino interaction events are identified, with backgrounds from other processes subtracted. We unfold the neutrino events into a fiducial volume corresponding to the sensitive regions of the FASER detector and interpret the results in two ways: We use the expected neutrino flux to measure the cross section, and we use the predicted cross section to measure the neutrino flux. Both results are presented in six bins of neutrino energy, achieving the first differential measurement in the TeV range. The observed distributions align with Standard Model predictions. Using this differential data, we extract the contributions of neutrinos from pion and kaon decays.

Motivated by recently observed anomalies in the flavour sector, we analyse the potential of measurements of top quarks at the Large Hadron Collider (LHC) to provide complementary constraints on interactions that shape low-energy precision investigations in the $B$ sector. The measurement of top quark properties, such as the top width and the abundant top pair production channels, are already reaching the percent level at this relatively early stage of the LHC phenomenology program. A focused analysis of four-fermion interactions, employing effective field theory without flavour structure assumptions and incorporating renormalization group evolution effects, bridges $B$ meson scale phenomena with key top quark measurements. We demonstrate that the LHC is increasingly competitive with, and complementary to, flavour physics constraints. Our results, which include a first comprehensive analysis of non-leptonic B decays in this context, suggest that the LHC's top physics program could serve as a valuable, complementary tool in the search for physics beyond the Standard Model within the flavour sector., Comment: 8 pages, 3 figures, 1 table. v2: Added references and small improvements to the discussion

The tau-function for quad-equations from the ABS classification is briefly explained. It is an auxiliary variable that systematically linearises the Backlund chain. Many equations have the same tau function and are unified by transformations constructed on that basis. Simple examples are included., Comment: 15 pages, corrected a faulty gauge factor in Section 4, other minor corrections

The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are characterised by high-energy deposits in the electromagnetic calorimeter and no signal in the veto scintillators. One event is observed, compared to a background expectation of $0.44 \pm 0.39$ events, which is entirely dominated by neutrino interactions. World-leading constraints on ALPs are obtained for masses up to $300~\text{MeV}$ and couplings to the Standard Model W gauge boson, $g_{aWW}$, around $10^{-4}$ GeV$^{-1}$, testing a previously unexplored region of parameter space. Other new particle models that lead to the same experimental signature, including ALPs coupled to gluons or photons, U(1)$_B$ gauge bosons, up-philic scalars, and a Type-I two-Higgs doublet model, are also considered for interpretation, and new constraints on previously viable parameter space are presented in this paper., Comment: 37 pages, 22 figures

We demonstrate efficient in-plane optical fiber collection of single photon emission from quantum dots embedded in photonic crystal cavities. This was achieved via adiabatic coupling between a tapered optical fiber and a tapered on-chip photonic waveguide coupled to the photonic crystal cavity. The collection efficiency of a dot in a photonic crystal cavity was measured to be 5 times greater via the tapered optical fiber compared to collection by a microscope objective lens above the cavity. The single photon source was also characterized by second order photon correlations measurements giving g(2)(0)=0.17 under non-resonant excitation. Numerical calculations demonstrate that the collection efficiency could be further increased by improving the dot-cavity coupling and by increasing the overlap length of the tapered fiber with the on-chip waveguide. An adiabatic coupling of near unity is predicted for an overlap length of 5 microns., Comment: 22 pages, 12 figures

We present the empirical dispersive optical model (DOM) as applied to direct nuclear reactions. The DOM links both scattering and bound-state experimental data through a dispersion relation which allows for fully-consistent, data-informed predictions for nuclei where such data exists. In particular, we review investigations of the electron-induced proton knockout reaction from both $^{40}$Ca and $^{48}$Ca in a distorted-wave impulse approximation (DWIA) utilizing the DOM for a fully-consistent description. Viewing these reactions through the lens of the DOM allows us to connect the documented quenching of spectroscopic factors with increased high-momentum proton content in neutron-rich nuclei. A similar DOM-DWIA description of the proton-induced knockout from $^{40}$Ca, however, does not currently fit in the consistent story of its electron-induced counterpart. With the main difference in the proton-induced case being the use of an effective proton-proton interaction, we suggest that a more sophisticated in-medium interaction would lead to consistent results., Comment: 28 pages, 11 figures, 4 tables. arXiv admin note: substantial text overlap with arXiv:1808.08895; text overlap with arXiv:2408.16097

A new nonlocal dispersive-optical-model analysis has been carried out for neutrons and protons in $^{48}$Ca that reproduces the weak-form-factor measurement of CREX. In addition to elastic-scattering angular distributions, total and reaction cross sections, single-particle energies, the neutron and proton numbers, and the charge distribution, the CREX-measured weak form factor has been fit to extract the neutron and proton self-energies both above and below the Fermi energy. The resulting single-particle propagators yield a weak form factor of $F_W = 0.125 \pm 0.05$ and a neutron skin of $R_\mathrm{skin} = 0.152 \pm 0.05$ fm, in good agreement with CREX. The rearrangement of the neutron distribution to accommodate such a thin neutron skin results in the high-momentum content of the neutrons exceeding that of the protons, in contrast to what is expected from high-energy two-nucleon knockout measurements by the CLAS collaboration and ab initio asymmetric matter calculations. The present analysis also emphasizes the importance of neutron experimental data in constraining weak charge observables necessary for a precise description of neutron densities. Notably, the neutron reaction cross section and further parity-violating experiments weak form factor measurements are essential to generate a unique way to determine the $^{48}$Ca neutron distribution in this framework., Comment: Revised (e,e'p) figure

Generative AI (GAI) holds great potential to improve software engineering productivity, but its untrustworthy outputs, particularly in code synthesis, pose significant challenges. The need for extensive verification and validation (V&V) of GAI-generated artifacts may undermine the potential productivity gains. This paper proposes a way of mitigating these risks by exploiting GAI's ability to generate multiple versions of code and tests to facilitate comparative analysis across versions. Rather than relying on the quality of a single test or code module, this "differential GAI" (D-GAI) approach promotes more reliable quality evaluation through version diversity. We introduce the Large-Scale Software Observatorium (LASSO), a platform that supports D-GAI by executing and analyzing large sets of code versions and tests. We discuss how LASSO enables rigorous evaluation of GAI-generated artifacts and propose its application in both software development and GAI research., Comment: This work has been accepted for publication in an upcoming issue of IEEE Software. This work has been submitted to the IEEE for possible publication

The $^3$He$(\alpha,\gamma)^7$Be radiative capture reaction plays a key role in the creation of elements in stars as well as in the production of solar neutrinos, the observation of which is one of the main tools to study the properties of our sun. Since accurate experimental measurements of this fusion cross section at solar energies are difficult due to the strong Coulomb repulsion between the reactants, the onus falls on theory to provide a robust means for extrapolating from the region where experimental data is available down to the desired astrophysical regime. We present the first microscopic calculations of $^3$He$(\alpha,\gamma)^7$Be with explicit inclusion of three-nucleon forces. Our prediction of the astrophysical $S$ factor qualitatively agrees with experimental data. We further incorporate experimental bound-state and scattering information in our calculation to arrive at a more quantitative description. This process reveals that our current model lacks sufficient repulsion in the $1/2^+$ channel of our model space to simultaneously reproduce elastic-scattering data. This deficit suggests that $^3$He$(\alpha,\gamma)^7$Be probes aspects of the nuclear force that are not currently well-constrained., Comment: 7 pages, 4 figures, 2 tables

An overview is presented of neutron skin predictions obtained using an empirical nonlocal dispersive optical model (DOM). The DOM links both scattering and bound-state experimental data through a subtracted dispersion relation which allows for fully-consistent, data-informed predictions for nuclei where such data exists. Large skins were predicted for both ${}^{48}$Ca ($R^{48}_\textrm{skin}=0.25 \pm 0.023$ fm in 2017) and $^{208}$Pb ($R^{208}_\textrm{skin}=0.25 \pm 0.05$ fm in 2020). While the DOM prediction in $^{208}$Pb is within 1$\sigma$ of the subsequent PREX-2 measurement, the DOM prediction in $^{48}$Ca is over 2$\sigma$ larger than the thin neutron skin resulting from CREX. From the moment it was revealed, the thin skin in ${}^{48}$Ca has puzzled the nuclear-physics community as no adequate theories simultaneously predict both a large skin in ${}^{208}$Pb and a small skin in ${}^{48}$Ca. The DOM is unique in its ability to treat both structure and reaction data on the same footing, providing a unique perspective on this $R_\textrm{skin}$ puzzle. It appears vital that more neutron data be measured in both the scattering and bound-state domain for ${}^{48}$Ca to clarify the situation., Comment: 21 pages, 9 figures, 2 tables. arXiv admin note: substantial text overlap with arXiv:1911.09020

Advanced probabilistic programming languages (PPLs) using hybrid particle filtering combine symbolic exact inference and Monte Carlo methods to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are encoded symbolically and variables that are encoded with sampled values, and the heuristics are not necessarily aligned with the developer's performance evaluation metrics. In this work, we present inference plans, a programming interface that enables developers to control the partitioning of random variables during hybrid particle filtering. We further present Siren, a new PPL that enables developers to use annotations to specify inference plans the inference system must implement. To assist developers with statically reasoning about whether an inference plan can be implemented, we present an abstract-interpretation-based static analysis for Siren for determining inference plan satisfiability. We prove the analysis is sound with respect to Siren's semantics. Our evaluation applies inference plans to three different hybrid particle filtering algorithms on a suite of benchmarks. It shows that the control provided by inference plans enables speed ups of 1.76x on average and up to 206x to reach a target accuracy, compared to the inference plans implemented by default heuristics; the results also show that inference plans improve accuracy by 1.83x on average and up to 595x with less or equal runtime, compared to the default inference plans. We further show that our static analysis is precise in practice, identifying all satisfiable inference plans in 27 out of the 33 benchmark-algorithm evaluation settings., Comment: v2: camera-ready version

Ocean submesoscales, flows with characteristic size around 10m--10km, are transitional between the larger, rotationally-constrained mesoscale and three-dimensional turbulence. In this paper we present simulations of a submesoscale ocean filament. The strong vertical transport associated with submesoscale fronts and filaments ventilates the deeper water and provides a route for the observed forward cascade of energy in the ocean. In our case, the filament is strongly sheared in both vertical and cross-filament directions and is unstable. Instability indeed dominates the early behaviour with a fast extraction of kinetic energy from the vertically sheared thermal wind. The filament is expected to be subject to instabilities, including symmetric and Kelvin-Helmholtz instabilities. An instability emerges with a circulation that does not match either, though symmetric modes become apparent later. The action of the instability is sufficiently rapid that the filament does not respond in a geostrophically balanced sense. Instead, it later exhibits vertically sheared near-inertial oscillations with higher amplitude as the initial minimum Richardson number decreases. Horizontal gradients strengthen only briefly as the fronts restratify. These unstable filaments can be generated by strong mixing events at pre-existing stable structures. The oscillations must also be considered when designing initial conditions for numerical experiments of submesoscale flows, where the initial state is intended to be balanced., Comment: Submitted to Journal of Fluid Mechanics. 11 pages, 6 figures, 1 table. Supplementary data consists of one movie "Movie 1" available at https://youtu.be/0FUPSQWpfe4

We identify several important and unsettled legal questions with profound ethical and societal implications arising from generative artificial intelligence (GenAI), focusing on its distinguishable characteristics from traditional software and earlier AI models. Our key contribution is formally identifying the issues that are unique to GenAI so scholars, practitioners, and others can conduct more useful investigations and discussions. While established legal frameworks, many originating from the pre-digital era, are currently employed in GenAI litigation, we question their adequacy. We argue that GenAI's unique attributes, including its general-purpose nature, reliance on massive datasets, and potential for both pervasive societal benefits and harms, necessitate a re-evaluation of existing legal paradigms. We explore potential areas for legal and regulatory adaptation, highlighting key issues around copyright, privacy, torts, contract law, criminal law, property law, and the First Amendment. Through an exploration of these multifaceted legal challenges, we aim to stimulate discourse and policy considerations surrounding GenAI, emphasizing a proactive approach to legal and ethical frameworks. While we refrain from advocating specific legal changes, we underscore the need for policymakers to carefully consider the issues raised. We conclude by summarizing key questions across these areas of law in a helpful table for easy reference., Comment: 14 pages

LLMs process text as sequences of tokens that roughly correspond to words, where less common words are represented by multiple tokens. However, individual tokens are often semantically unrelated to the meanings of the words/concepts they comprise. For example, Llama-2-7b's tokenizer splits the word "northeastern" into the tokens ['_n', 'ort', 'he', 'astern'], none of which correspond to semantically meaningful units like "north" or "east." Similarly, the overall meanings of named entities like "Neil Young" and multi-word expressions like "break a leg" cannot be directly inferred from their constituent tokens. Mechanistically, how do LLMs convert such arbitrary groups of tokens into useful higher-level representations? In this work, we find that last token representations of named entities and multi-token words exhibit a pronounced "erasure" effect, where information about previous and current tokens is rapidly forgotten in early layers. Using this observation, we propose a method to "read out" the implicit vocabulary of an autoregressive LLM by examining differences in token representations across layers, and present results of this method for Llama-2-7b and Llama-3-8B. To our knowledge, this is the first attempt to probe the implicit vocabulary of an LLM., Comment: 13 pages, 14 figures. Code and data at https://footprints.baulab.info/

Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI & Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually simpler, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate, Comment: Accepted to NLLP Workshop, EMNLP 2024

The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science., Comment: To appear in ACM Transactions on Software Engineering and Methodology, Special Issue "2030 Roadmap Software Engineering"

Stage IV surveys like LSST and Euclid present a unique opportunity to shed light on the nature of dark energy. However, their full constraining power cannot be unlocked unless accurate predictions are available at all observable scales. Currently, only the linear regime is well understood in models beyond $\Lambda$CDM: on the nonlinear scales, expensive numerical simulations become necessary, whose direct use is impractical in the analyses of large datasets. Recently, machine learning techniques have shown the potential to break this impasse: by training emulators, we can predict complex data fields in a fraction of the time it takes to produce them. In this work, we present a field-level emulator capable of turning a $\Lambda$CDM N-body simulation into one evolved under $f(R)$ gravity. To achieve this, we build on the map2map neural network, using the strength of modified gravity $|f_{R_0}|$ as style parameter. We find that our emulator correctly estimates the changes it needs to apply to the positions and velocities of the input N-body particles to produce the target simulation. We test the performance of our network against several summary statistics, finding $1\%$ agreement in the power spectrum up to $k \sim 1$ $h/$Mpc, and $1.5\%$ agreement against the independent boost emulator eMantis. Although the algorithm is trained on fixed cosmological parameters, we find it can extrapolate to models it was not trained on. Coupled with available field-level emulators and simulation suites for $\Lambda$CDM, our algorithm can be used to constrain modified gravity in the large-scale structure using full information available at the field level., Comment: 17 pages, 11 figures; version accepted on MNRAS

The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities. AI's transformative impact on data analysis, particularly derived from EO platforms, holds great promise in addressing global challenges such as environmental monitoring, disaster response and climate change analysis. However, the rapid integration of AI necessitates a careful examination of the responsible dimensions inherent in its application within these domains. In this paper, we represent a pioneering effort to systematically define the intersection of AI and EO, with a central focus on responsible AI practices. Specifically, we identify several critical components guiding this exploration from both academia and industry perspectives within the EO field: AI and EO for social good, mitigating unfair biases, AI security in EO, geo-privacy and privacy-preserving measures, as well as maintaining scientific excellence, open data, and guiding AI usage based on ethical principles. Furthermore, the paper explores potential opportunities and emerging trends, providing valuable insights for future research endeavors.

The exploration of carbon-to-oxygen ratios has yielded intriguing insights into the composition of close-in giant exoplanets, giving rise to a distinct classification: carbon-rich planets, characterized by a carbon-to-oxygen ratio $\ge$ 1 in their atmospheres, as opposed to giant planets exhibiting carbon-to-oxygen ratios close to the protosolar value. In contrast, despite numerous space missions dispatched to the outer solar system and the proximity of Jupiter, Saturn, Uranus, and Neptune, our understanding of the carbon-to-oxygen ratio in these giants remains notably deficient. Determining this ratio is crucial as it serves as a marker linking a planet's volatile composition directly to its formation region within the disk. This article provides an overview of the current understanding of the carbon-to-oxygen ratio in the four gas giants of our solar system and explores why there is yet no definitive dismissal of the possibility that Jupiter, Saturn, Uranus, or Neptune could be considered carbon-rich planets. Additionally, we delve into the three primary formation scenarios proposed in existing literature to account for a bulk carbon-to-oxygen ratio $\ge$ 1 in a giant planet. A significant challenge lies in accurately inferring the bulk carbon-to-oxygen ratio of our solar system's gas giants. Retrieval methods involve integrating in situ measurements from entry probes equipped with mass spectrometers and remote sensing observations conducted at microwave wavelengths by orbiters. However, these methods fall short of fully discerning the deep carbon-to-oxygen abundance in the gas giants due to their limited probing depth, typically within the 10-100 bar range., Comment: Space Science Reviews, in press

The integration of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) in academia has spurred a global discourse on their potential pedagogical benefits and ethical considerations. Positive reactions highlight some potential, such as collaborative creativity, increased access to education, and empowerment of trainers and trainees. However, negative reactions raise concerns about ethical complexities, balancing innovation and academic integrity, unequal access, and misinformation risks. Through a systematic survey and text-mining-based analysis of global and national directives, insights from independent research, and eighty university-level guidelines, this study provides a nuanced understanding of the opportunities and challenges posed by GAI and LLMs in education. It emphasizes the importance of balanced approaches that harness the benefits of these technologies while addressing ethical considerations and ensuring equitable access and educational outcomes. The paper concludes with recommendations for fostering responsible innovation and ethical practices to guide the integration of GAI and LLMs in academia.

Representation learning and \emph{de novo} generation of proteins are pivotal computational biology tasks. Whilst natural language processing (NLP) techniques have proven highly effective for protein sequence modelling, structure modelling presents a complex challenge, primarily due to its continuous and three-dimensional nature. Motivated by this discrepancy, we introduce an approach using a vector-quantized autoencoder that effectively tokenizes protein structures into discrete representations. This method transforms the continuous, complex space of protein structures into a manageable, discrete format with a codebook ranging from 4096 to 64000 tokens, achieving high-fidelity reconstructions with backbone root mean square deviations (RMSD) of approximately 1-5 \AA. To demonstrate the efficacy of our learned representations, we show that a simple GPT model trained on our codebooks can generate novel, diverse, and designable protein structures. Our approach not only provides representations of protein structure, but also mitigates the challenges of disparate modal representations and sets a foundation for seamless, multi-modal integration, enhancing the capabilities of computational methods in protein design.