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In this paper, we detail recent and current work that is being carried out to fabricate and advance novel SiC UV instrumentation that is aimed at enabling more sensitive measurements across numerous disciplines, with a short discussion of the promise such detectors may hold for the Habitable Worlds Observatory. We discuss SiC instrument development progress that is being carried out under multiple NASA grants, including several PICASSO and SBIR grants, as well as an ECI grant. Testing of pixel design, properties and layout as well as maturation of the integration scheme developed through these efforts provide key technology and engineering advancement for potential HWO detectors. Achieving desired noise characteristics, responsivity, and validating operation of SiC detectors using standard read out techniques offers a compelling platform for operation of denser and higher dimensionality SiC photodiode arrays of interest for use in potential HWO Coronagraph, Spectrograph, and High Resolution Imaging Instruments. We incorporate these SiC detector properties into a simulation of potential NUV exoplanet observations by HWO using SiC detectors and also discuss potential application to HWO., Comment: 8 figures, Submitted to JATIS

We present two developments which enhance the predictive power of empirical shell model Hamiltonians for cases in which calibration data is sparse. A recent improvement in the ab initio derivation of effective Hamiltonians leads to a much better starting point for the optimization procedure. In addition, we introduce a protocol to avoid over-fitting, enabling a more reliable extrapolation beyond available data. These developments will enable more robust predictions for exotic isotopes produced at rare isotope beam facilities and in astrophysical environments., Comment: 11 pages, 6 figures

We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM). These models offer comprehensive coverage across multiple risk dimensions, including social bias, profanity, violence, sexual content, unethical behavior, jailbreaking, and hallucination-related risks such as context relevance, groundedness, and answer relevance for retrieval-augmented generation (RAG). Trained on a unique dataset combining human annotations from diverse sources and synthetic data, Granite Guardian models address risks typically overlooked by traditional risk detection models, such as jailbreaks and RAG-specific issues. With AUC scores of 0.871 and 0.854 on harmful content and RAG-hallucination-related benchmarks respectively, Granite Guardian is the most generalizable and competitive model available in the space. Released as open-source, Granite Guardian aims to promote responsible AI development across the community. https://github.com/ibm-granite/granite-guardian

It has been known for several decades that classical alternating links in the 3-sphere have nice hyperbolic geometric properties. Recent work generalises such results to give hyperbolic geometry of links with alternating projections onto any surface in very general 3-manifolds. However, the most general results require an orientable projection surface. In this paper, we extend to alternating links on nonorientable projection surfaces. As an application, we study Klein bottly alternating links in prism manifolds, which are a natural generalisation of Adams' toroidally alternating links in lens spaces., Comment: 15 pages, 6 figures

We prove that for any knot $K$, there exists a one-vertex triangulation of the $3$-sphere containing an edge forming $K$. The proof is constructive, and based on fully augmented links. We use our method to produce ``complicated'' simplicial triangulations of the $3$-sphere that we show are smallest possible, up to a constant multiplicative factor., Comment: 19 pages, 13 figures

Misclassification in binary outcomes is not uncommon and statistical methods to investigate its impact on policy-driving study results are lacking. While misclassifying binary outcomes is a statistically ubiquitous phenomena, we focus on misclassification in a public health application: vaccinations. One such study design in public health that addresses policy is the cluster controlled randomized trial (CCRT). A CCRT that measures the impact of a novel behavioral intervention on increasing vaccine uptake can be severely biased when the supporting data are incomplete vaccination records. In particular, these vaccine records more often may be prone to negative misclassification, that is, a clinic's record of an individual patient's vaccination status may be unvaccinated when, in reality, this patient was vaccinated outside of the clinic. With large nation-wide endeavors to encourage vaccinations without a gold-standard vaccine record system, sensitivity analyses that incorporate misclassification rates are promising for robust inference. In this work we introduce a novel extension of Bayesian logistic regression where we perturb the clinic size and vaccination count with random draws from expert-elicited prior distributions. These prior distributions represent the misclassification rates for each clinic that stochastically add unvaccinated counts to the observed vaccinated counts. These prior distributions are assigned for each clinic (the first level in a group-level randomized trial). We demonstrate this method with a data application from a CCRT evaluating the influence of a behavioral intervention on vaccination uptake among U.S. veterans. A simulation study is carried out demonstrating its estimation properties.

Climate models serve as critical tools for evaluating the effects of climate change and projecting future climate scenarios. However, the reliance on numerical simulations of physical equations renders them computationally intensive and inefficient. While deep learning methodologies have made significant progress in weather forecasting, they are still unstable for climate emulation tasks. Here, we propose PACER, a lightweight 684K parameter Physics Informed Uncertainty Aware Climate Emulator. PACER emulates temperature and precipitation stably for 86 years while only being trained on greenhouse gas emissions data. We incorporate a fundamental physical law of advection-diffusion in PACER accounting for boundary conditions and empirically estimating the diffusion co-efficient and flow velocities from emissions data. PACER has been trained on 15 climate models provided by ClimateSet outperforming baselines across most of the climate models and advancing a new state of the art in a climate diagnostic task.

Despite the impressive adaptability of large language models (LLMs), challenges remain in ensuring their security, transparency, and interpretability. Given their susceptibility to adversarial attacks, LLMs need to be defended with an evolving combination of adversarial training and guardrails. However, managing the implicit and heterogeneous knowledge for continuously assuring robustness is difficult. We introduce a novel approach for assurance of the adversarial robustness of LLMs based on formal argumentation. Using ontologies for formalization, we structure state-of-the-art attacks and defenses, facilitating the creation of a human-readable assurance case, and a machine-readable representation. We demonstrate its application with examples in English language and code translation tasks, and provide implications for theory and practice, by targeting engineers, data scientists, users, and auditors., Comment: To be published in xAI 2024, late-breaking track

The EU AI Act (EUAIA) introduces requirements for AI systems which intersect with the processes required to establish adversarial robustness. However, given the ambiguous language of regulation and the dynamicity of adversarial attacks, developers of systems with highly complex models such as LLMs may find their effort to be duplicated without the assurance of having achieved either compliance or robustness. This paper presents a functional architecture that focuses on bridging the two properties, by introducing components with clear reference to their source. Taking the detection layer recommended by the literature, and the reporting layer required by the law, we aim to support developers and auditors with a reasoning layer based on knowledge augmentation (rules, assurance cases, contextual mappings). Our findings demonstrate a novel direction for ensuring LLMs deployed in the EU are both compliant and adversarially robust, which underpin trustworthiness., Comment: Accepted in the VECOMP 2024 workshop

Large language models are prone to misuse and vulnerable to security threats, raising significant safety and security concerns. The European Union's Artificial Intelligence Act seeks to enforce AI robustness in certain contexts, but faces implementation challenges due to the lack of standards, complexity of LLMs and emerging security vulnerabilities. Our research introduces a framework using ontologies, assurance cases, and factsheets to support engineers and stakeholders in understanding and documenting AI system compliance and security regarding adversarial robustness. This approach aims to ensure that LLMs adhere to regulatory standards and are equipped to counter potential threats., Comment: Accepted in the AI Act Workshop

This paper presents an approach to developing assurance cases for adversarial robustness and regulatory compliance in large language models (LLMs). Focusing on both natural and code language tasks, we explore the vulnerabilities these models face, including adversarial attacks based on jailbreaking, heuristics, and randomization. We propose a layered framework incorporating guardrails at various stages of LLM deployment, aimed at mitigating these attacks and ensuring compliance with the EU AI Act. Our approach includes a meta-layer for dynamic risk management and reasoning, crucial for addressing the evolving nature of LLM vulnerabilities. We illustrate our method with two exemplary assurance cases, highlighting how different contexts demand tailored strategies to ensure robust and compliant AI systems., Comment: Accepted to the ASSURE 2024 workshop

We construct a family of Hamiltonians whose phase diagram is guaranteed to have a single phase transition, yet the location of this phase transition is uncomputable. The Hamiltonians $H(\phi)$ describe qudits on a two-dimensional square lattice with translationally invariant, nearest-neighbour interactions tuned by a continuous parameter $\phi\in(0,1]$. For all $\phi\in(0,1]$, $H(\phi)$ is in one of two phases, one a gapless phase, the other a gapped phase. The phase transition occurs when $\phi$ equals the Chaitin's constant $\Omega$, a well-defined real number that encodes the Halting problem, and hence is uncomputable for Turing machines and undecidable for any consistent recursive axiomatization of mathematics. Our result implies that no general algorithm exists to determine the phase diagrams even under the promise that the phase diagram is exceedingly simple, and illustrates how uncomputable numbers may manifest in physical systems.

Accurately and efficiently predicting the infrared (IR) spectra of a molecule can provide insights into the structure-properties relationships of molecular species, which has led to a proliferation of machine learning tools designed for this purpose. However, earlier studies have focused primarily on obtaining normalized IR spectra, which limits their potential for a comprehensive analysis of molecular behavior in the IR range. For instance, to fully understand and predict the optical properties, such as the transparency characteristics, it is necessary to predict the molar absorptivity IR spectra instead. Here, we propose a graph-based communicative message passing neural network (CMPNN) algorithm that can predict both the peak positions and absolute intensities corresponding to density functional theory (DFT) calculated molar absorptivities in the IR domain. By modifying existing spectral loss functions, we show that our method is able to predict with DFT-accuracy level the IR molar absorptivities of a series of hydrocarbons containing up to ten carbon atoms and apply the model to a set of larger molecules. We also compare the predicted spectra with those generated by the direct message passing neural network (DMPNN). The results suggest that both algorithms demonstrate similar predictive capabilities for hydrocarbons, indicating that either model could be effectively used in future research on spectral prediction for such systems.

The proliferation of Large Language Models (LLMs) in diverse applications underscores the pressing need for robust security measures to thwart potential jailbreak attacks. These attacks exploit vulnerabilities within LLMs, endanger data integrity and user privacy. Guardrails serve as crucial protective mechanisms against such threats, but existing models often fall short in terms of both detection accuracy, and computational efficiency. This paper advocates for the significance of jailbreak attack prevention on LLMs, and emphasises the role of input guardrails in safeguarding these models. We introduce MoJE (Mixture of Jailbreak Expert), a novel guardrail architecture designed to surpass current limitations in existing state-of-the-art guardrails. By employing simple linguistic statistical techniques, MoJE excels in detecting jailbreak attacks while maintaining minimal computational overhead during model inference. Through rigorous experimentation, MoJE demonstrates superior performance capable of detecting 90% of the attacks without compromising benign prompts, enhancing LLMs security against jailbreak attacks.

As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. Despite growing academic interest in adversarial risks for generative AI, there is limited guidance tailored for practitioners to assess and mitigate these challenges in real-world environments. To address this, our contributions include: (1) a practical examination of red- and blue-teaming strategies for securing generative AI, (2) identification of key challenges and open questions in defense development and evaluation, and (3) the Attack Atlas, an intuitive framework that brings a practical approach to analyzing single-turn input attacks, placing it at the forefront for practitioners. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.

We report measurements of ten custom-made high-homogeneity LYSO crystals. The investigation is motivated by the need for a compact, high-resolution, and fast electromagnetic calorimeter for a new rare pion decay experiment. Each $2.5\times 2.5 \times 18$ cm$^3$ crystal was first characterized for general light yield properties and then its longitudinal response uniformity and energy resolution were measured using low-energy gamma sources. The ten crystals were assembled as an array and subjected to a 30 - 100 MeV positron beam with excellent momentum definition. The energy and timing resolutions were measured as a function of energy, and the spatial resolution was determined at 70 MeV. An additional measurement using monoenergetic 17.6 MeV gammas produced through a p-Li resonance was later made after the photosensors used in positron testing were improved. As an example of the results, the energy resolution at 70 MeV of 1.52 $\pm$ 0.03% is more than two times better than reported results using previous generation LYSO crystals., Comment: Revision including updated waveform analysis yielding improved energy resolution and slightly improved time resolution

Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD). By training these potentials on data generated from ab initio methods, their averaged predictions can exhibit comparable performance to ab initio methods at a fraction of the cost. However, insufficient training sets might lead to an improper description of the dynamics in strongly anharmonic materials, because critical effects might be overlooked in relevant cases, or only incorrectly captured, or hallucinated by the MLIP when they are not actually present. In this work, we show that an active learning scheme that combines MD with MLIPs (MLIP-MD) and uncertainty estimates can avoid such problematic predictions. In short, efficient MLIP-MD is used to explore configuration space quickly, whereby an acquisition function based on uncertainty estimates and on energetic viability is employed to maximize the value of the newly generated data and to focus on the most unfamiliar but reasonably accessible regions of phase space. To verify our methodology, we screen over 112 materials and identify 10 examples experiencing the aforementioned problems. Using CuI and AgGaSe$_2$ as archetypes for these problematic materials, we discuss the physical implications for strongly anharmonic effects and demonstrate how the developed active learning scheme can address these issues., Comment: 15 pages, 13 figures

The study of rod complements is motivated by rod packing structures in crystallography. We view them as complements of links comprised of Euclidean geodesics in the 3-torus. Recent work of the second author classifies when such rod complements admit hyperbolic structures, but their geometric properties are yet to be well understood. In this paper, we provide upper and lower bounds for the volumes of all hyperbolic rod complements in terms of rod parameters, and show that these bounds may be loose in general. We introduce better and asymptotically sharp volume bounds for a family of rod complements. The bounds depend only on the lengths of the continued fractions formed from the rod parameters., Comment: 19 pages, 4 figures. V2: More references added, minor updates on introduction

The Dragonfly Spectral Line Mapper is an innovative all-refracting telescope designed to carry out ultra-low surface brightness wide-field mapping of visible wavelength line emission. Equipped with ultranarrowband (0.8 nm bandwidth) filters mounted in Dragonfly Filter-Tilter instrumentation, the Dragonfly Spectral Line Mapper maps H$\alpha$, [NII]$\lambda$6583, and [OIII]$\lambda$5007 line emission produced by structures with sizes ranging from $\sim$1 to 1000 kpc in the local Universe. These spatial scales encompass that of the exceedingly diffuse and faintly radiating circumgalactic medium, which is singularly difficult to detect with conventional mirror-based telescope instrumentation. Extremely careful control of systematics is required to directly image these large scale structures, necessitating high fidelity sky background subtraction, wavelength calibration, and specialized flat-fielding methods. In this paper, we discuss the on-sky performance of the Dragonfly Spectral Line Mapper with these methods in place., Comment: 10 pages, 5 figures, SPIE Astronomical Telescopes and Instrumentation 2024 Proceedings

In the presence of anisotropic neutrino and antineutrino fluxes, the quantum kinetic equations drive coherent oscillations in neutrino helicity, frequently referred to as spin oscillations. These oscillations depend directly on the absolute mass scale and Majorana phase, but are usually too transient to produce important effects. In this paper we present a full momentum-space analysis of Majorana neutrino spin oscillations in a snapshot of a three-dimensional neutron star merger simulation. We find an interesting angular dependence that allows for that resonant and adiabatic oscillations to occur along specific directions in a large volume of the merger remnant. The solid angle spanned by these directions is extremely narrow in general. We then analyze spin transformation in the presence of flavor transformation by characterizing how the effect's resonance and timescale change during a fast flavor instability. For this analysis, we derive a generalized resonance condition that poses a restrictive requirement for resonance to exist in any flavor channel. We determine that spin oscillations at all locations in the merger snapshot have a length scale that is too large for significant oscillations to be expected even where there exist resonant and adiabatic directions., Comment: 23 pages, 14 figures; corrected a formatting issue, added references to match final PRD publication

This paper employs knot invariants and results from hyperbolic geometry to develop a practical procedure for checking the cosmetic surgery conjecture on any given one-cusped manifold. This procedure has been used to establish the following computational results. First, we verify that all knots up to 19 crossings, and all one-cusped 3-manifolds in the SnapPy census, do not admit any purely cosmetic surgeries. Second, we check that a hyperbolic knot with at most 15 crossings only admits chirally cosmetic surgeries when the knot itself is amphicheiral. Third, we enumerate all knots up to 13 crossings that share a common Dehn fillings with the figure-8 knot. The code that verifies these results is publicly available on GitHub., Comment: 29 pages, 1 figure. v2 has updated discussion and references

Operational weather forecasting system relies on computationally expensive physics-based models. Recently, transformer based models have shown remarkable potential in weather forecasting achieving state-of-the-art results. However, transformers are discrete and physics-agnostic models which limit their ability to learn the continuous spatio-temporal features of the dynamical weather system. We address this issue with STC-ViT, a Spatio-Temporal Continuous Vision Transformer for weather forecasting. STC-ViT incorporates the continuous time Neural ODE layers with multi-head attention mechanism to learn the continuous weather evolution over time. The attention mechanism is encoded as a differentiable function in the transformer architecture to model the complex weather dynamics. Further, we define a customised physics informed loss for STC-ViT which penalize the model's predictions for deviating away from physical laws. We evaluate STC-ViT against operational Numerical Weather Prediction (NWP) model and several deep learning based weather forecasting models. STC-ViT, trained on 1.5-degree 6-hourly data, demonstrates computational efficiency and competitive performance compared to state-of-the-art data-driven models trained on higher-resolution data for global forecasting.

Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research., Comment: Review, outlook, roadmap, perspective

The main result of this paper is an edge-coloured version of Tutte's $f$-factor theorem. We give a necessary and sufficient condition for an edge-coloured graph $G^c$ to have a properly coloured $f$-factor. We state and prove our result in terms of an auxiliary graph $G_f^c$ which has a 1-factor if and only if $G^c$ has a properly coloured $f$-factor; this is analogous to the "short proof" of the $f$-factor theorem given by Tutte in 1954. An alternative statement, analogous to the original $f$-factor theorem, is also given. We show that our theorem generalises the $f$-factor theorem; that is, the former implies the latter. We consider other properties of edge-coloured graphs, and show that similar results are unlikely for $f$-factors with rainbow components and distance-$d$-coloured $f$-factors, even when $d=2$ and the number of colours used is asymptotically minimal., Comment: 18 pages, 5 figures

We find polynomial upper bounds on the number of isotopy classes of connected essential surfaces embedded in many cusped 3-manifolds and their Dehn fillings. Our bounds are universal, in the sense that we obtain the same explicit formula for all 3-manifolds that we consider, with the formula dependent on the Euler characteristic of the surface and similar numerical quantities encoding topology of the ambient 3-manifold. Universal and polynomial bounds have been obtained previously for classical alternating links in the 3-sphere and their Dehn fillings, but only for surfaces that are closed or spanning. Here, we consider much broader classes of 3-manifolds and all topological types of surfaces. The 3-manifolds are called weakly generalized alternating links; they include, for example, many links that are not classically alternating and/or do not lie in the 3-sphere, many virtual links and toroidally alternating links., Comment: 28 pages, 3 figures. V2: Updated title, abstract, and introduction