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As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical properties (strength, ductility, toughness) of most materials that can hinder their use as part of the infrastructure of a hydrogen-based economy. Enabling H mapping, including local hydrogen concentration analyses at specific microstructural features, is essential for understanding the multiple ways that H affect the properties of materials, including for instance embrittlement mechanisms and their synergies, but also spatial mapping and quantification of hydrogen isotopes is essential to accurately predict tritium inventory of future fusion power plants, ensuring their safe and efficient operation for example. Atom probe tomography (APT) has the intrinsic capabilities for detecting hydrogen (H), and deuterium (D), and in principle the capacity for performing quantitative mapping of H within a material's microstructure. Yet the accuracy and precision of H analysis by APT remain affected by the influence of residual hydrogen from the ultra-high vacuum chamber that can obscure the signal of H from within the material, along with a complex field evaporation behavior. The present article reports the essence of discussions at a focused workshop held at the Max-Planck Institute for Sustainable Materials in April 2024. The workshop was organized to pave the way to establishing best practices in reporting APT data for the analysis of H. We first summarize the key aspects of the intricacies of H analysis by APT and propose a path for better reporting of the relevant data to support interpretation of APT-based H analysis in materials.

Several long-period binaries with a carbon-rich Wolf-Rayet star and an O star produce dust in their wind collisions. In eccentric binaries, this is seen most strongly near periastron passage. The exact conditions leading to dust creation require orbital properties to be determined, which is difficult owing to their long periods. Recently, the binary system WR 125 (WC7+O9III) began a dust creation episode seen through an infrared outburst first detected by NEOWISE-R, which was the first outburst detected since 1991. We present new near- and mid-infrared photometry, which we use to show consistency between the two outbursts and derive an orbital period of 28.12$^{+0.10}_{-0.05}$ yr. We use a long time-series of optical spectra to place the first constraints on its orbital elements, on the assumption that this system will produce dust near periastron. The orbit has a mild eccentricity of 0.29$\pm$0.12 and is only derived for the Wolf-Rayet component, as the O star's radial velocities have noise that is likely larger than the expected semi-amplitude of the orbit. We also present SOFIA/FORCAST grism spectroscopy to examine the infrared spectral energy distribution (SED) of the dust during this outburst, comparing its properties to other WCd binaries, deriving a dust temperature of 580 K in 2021. This collection of observations will allow us to plan future observations of this system and place the system in the context of dust-creating Wolf-Rayet binaries., Comment: accepted to ApJ

Compiling programs to an instruction set architecture (ISA) requires a set of rewrite rules that map patterns consisting of compiler instructions to patterns consisting of ISA instructions. We synthesize such rules by constructing SMT queries, whose solutions represent two functionally equivalent programs. These two programs are interpreted as an instruction selection rewrite rule. Existing work is limited to single-instruction ISA patterns, whereas our solution does not have that restriction. Furthermore, we address inefficiencies of existing work by developing two optimized algorithms. The first only generates unique rules by preventing synthesis of duplicate and composite rules. The second only generates lowest-cost rules by preventing synthesis of higher-cost rules. We evaluate our algorithms on multiple ISAs. Without our optimizations, the vast majority of synthesized rewrite rules are either duplicates, composites, or higher cost. Our optimizations result in synthesis speed-ups of up to 768x and 4004x for the two algorithms., Comment: 9 pages, 2 figures, in submission to FMCAD24, fixed small bugs in results generation, improved baseline comparison, improved background and writing clarity

Generative Artificial Intelligence (AI) is integrated into everyday technology, including news, education, and social media. AI has further pervaded private conversations as conversational partners, auto-completion, and response suggestions. As social media becomes young people's main method of peer support exchange, we need to understand when and how AI can facilitate and assist in such exchanges in a beneficial, safe, and socially appropriate way. We asked 622 young people to complete an online survey and evaluate blinded human- and AI-generated responses to help-seeking messages. We found that participants preferred the AI-generated response to situations about relationships, self-expression, and physical health. However, when addressing a sensitive topic, like suicidal thoughts, young people preferred the human response. We also discuss the role of training in online peer support exchange and its implications for supporting young people's well-being. Disclaimer: This paper includes sensitive topics, including suicide ideation. Reader discretion is advised.

Objective: Diet quality is important for people with multiple sclerosis (MS), but conflicting online information causes them confusion. People with MS want evidence-based MS-specific information to help them make healthy dietary changes, and we co-designed an asynchronous, online nutrition education program (Eating Well with MS) with the MS community. Our aim was to determine the feasibility of Eating Well with MS. Methods: We used a single-arm pre-post design. The feasibility trial was a nine-week intervention with adults with confirmed MS. Feasibility outcomes: 1) demand (recruitment); 2) practicality (completion); 3) acceptability (Intrinsic Motivation Inventory: interest/enjoyment and value/usefulness subscales); and 4) limited efficacy testing (Diet Habits Questionnaire (DHQ); Critical Nutrition Literacy Tool (CNLT); Food Literacy Behaviour Checklist (FLBC)). Results: The recruitment target (n=70) was reached. 87% completed at least one module and 57% completed the full program (five modules). The median interest/enjoyment rating was 5 out of 7 and median value/usefulness rating was 6 out of 7 (where 7 = very true). Compared to pre-program, participants who completed any of the program had statistically significantly improved DHQ, CNLT, and FLBC scores. Conclusion: Eating Well with MS was well received by the MS community and improved their dietary behaviours; demonstrating feasibility. Our findings support the use of co-design methods when developing resources to improve dietary behaviours.

Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limitations: they do not necessarily require anonymization mechanisms like differential privacy (DP), and provide limited protections against a potentially malicious service provider. Adding DP to a basic FLA system currently requires either adding excessive noise to each device's updates, or assuming an honest service provider that correctly implements the mechanism and only uses the privatized outputs. Secure multiparty computation (SMPC) -based oblivious aggregations can limit the service provider's access to individual user updates and improve DP tradeoffs, but the tradeoffs are still suboptimal, and they suffer from scalability challenges and susceptibility to Sybil attacks. This paper introduces a novel system architecture that leverages trusted execution environments (TEEs) and open-sourcing to both ensure confidentiality of server-side computations and provide externally verifiable privacy properties, bolstering the robustness and trustworthiness of private federated computations.

Atmospheric interaction with light has been an area of fascination for many researchers over the last century. Environmental conditions, such as temperature and wind speed, heavily influence the complex and rapidly varying optical distortions propagating optical fields experience. The continuous random phase fluctuations commonly make deciphering the exact origins of specific optical aberrations challenging. The generation of eddies is a major contributor to atmospheric turbulence, similar in geometric structure to optical vortices that sit at the centre of OAM beams. Decomposing the received optical fields into OAM provides a unique spatial similarity that can be used to analyse turbulent channels. In this work, we present a novel mode decomposition assisted machine learning approach that reveals trainable features in the distortions of vortex beams that allow for effective environmental monitoring. This novel technique can be used reliably with Support Vector Machine regression models to measure temperature variations of 0.49C and wind speed variations of 0.029 m/s over a 36m experimental turbulent free-space channels with controllable and verifiable temperature and wind speed with short 3s measurement. The predictable nature of these findings could indicate the presence of an underlying physical relationship between environmental conditions that lead to specific eddy formation and the OAM spiral spectra.

Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be attributed to violation of one or more conversational principles. By drawing upon extensive research from both the social science and AI communities, we propose a set of maxims -- quantity, quality, relevance, manner, benevolence, and transparency -- for describing effective human-AI conversation. We first justify the applicability of the first four maxims (from Grice) in the context of human-AI interactions. We then argue that two new maxims, benevolence (concerning the generation of, and engagement with, harmful content) and transparency (concerning recognition of one's knowledge boundaries, operational constraints, and intents), are necessary for addressing behavior unique to modern human-AI interactions. We evaluate the degree to which various language models are able to understand these maxims and find that models possess an internal prioritization of principles that can significantly impact their ability to interpret the maxims accurately.

Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (training cost, API access, data availability, etc.), it may not always be feasible to impose direct safety constraints on a deployed model. Therefore, an efficient and reliable alternative is required. To this end, we present our ongoing efforts to create and deploy a library of detectors: compact and easy-to-build classification models that provide labels for various harms. In addition to the detectors themselves, we discuss a wide range of uses for these detector models - from acting as guardrails to enabling effective AI governance. We also deep dive into inherent challenges in their development and discuss future work aimed at making the detectors more reliable and broadening their scope.

On September 26, 2022, NASA's Double Asteroid Redirection Test (DART) mission successfully impacted Dimorphos, the natural satellite of the binary near-Earth asteroid (65803) Didymos. Numerical simulations of the impact provide a means to explore target surface material properties and structures, consistent with the observed momentum deflection efficiency, ejecta cone geometry, and ejected mass. Our simulation, which best matches observations, indicates that Dimorphos is weak, with a cohesive strength of less than a few pascals (Pa), similar to asteroids (162173) Ryugu and (101955) Bennu. We find that a bulk density of Dimorphos, rhoB, lower than 2400 kg/m3, and a low volume fraction of boulders (<40 vol%) on the surface and in the shallow subsurface, are consistent with measured data from the DART experiment. These findings suggest Dimorphos is a rubble pile that might have formed through rotational mass shedding and re-accumulation from Didymos. Our simulations indicate that the DART impact caused global deformation and resurfacing of Dimorphos. ESA's upcoming Hera mission may find a re-shaped asteroid, rather than a well-defined crater.

Impacts are critical to producing the aqueous environments necessary to stimulate prebiotic chemistry on Titan's surface. Furthermore, organic hazes resting on the surface are a likely feedstock of biomolecules. In this work, we conduct impact experiments on laboratory-produced organic haze particles and haze/sand mixtures and analyze these samples for life's building blocks. Samples of unshocked haze and sand particles are also analyzed to determine the change in biomolecule concentrations and distributions from shocking. Across all samples, we detect seven nucleobases, nine proteinogenic amino acids, and five other biomolecules (e.g., urea) using a blank subtraction procedure to eliminate signals due to contamination. We find that shock pressures of 13 GPa variably degrade nucleobases, amino acids, and a few other organics in haze particles and haze/sand mixtures; however, certain individual biomolecules become enriched or are even produced from these events. Xanthine, threonine, and aspartic acid are enriched or produced in impact experiments containing sand, suggesting these minerals may catalyze the production of these biomolecules. On the other hand, thymine and isoleucine/norleucine are enriched or produced in haze samples containing no sand, suggesting catalytic grains are not necessary for all impact shock syntheses. Uracil, glycine, proline, cysteine, and tyrosine are the most unstable to impact-related processing. These experiments suggest that impacts alter biomolecule distributions on Titan's surface, and that organic hazes co-occurring with fine-grained material on the surface may provide an initial source for further prebiotic chemistry on Titan., Comment: Accepted to PSJ. 13 pages, 3 tables, 7 figures

Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require human responses which are expensive to acquire or use pairs of LLMs to evaluate each other which can be unreliable. In this paper, we provide a novel perspective where, given a dataset of prompts (viz. questions, instructions, etc.) and a set of LLMs, we rank them without access to any ground truth or reference responses. Inspired by real life where both an expert and a knowledgeable person can identify a novice our main idea is to consider triplets of models, where each one of them evaluates the other two, correctly identifying the worst model in the triplet with high probability. We also analyze our idea and provide sufficient conditions for it to succeed. Applying this idea repeatedly, we propose two methods to rank LLMs. In experiments on different generative tasks (summarization, multiple-choice, and dialog), our methods reliably recover close to true rankings without reference data. This points to a viable low-resource mechanism for practical use., Comment: Accepted to ACL 2024