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Silica polymorphs and zeolites are fundamental to a wide range of industrial applications owing to their diverse structural characteristics, thermodynamic and mechanical stability under varying conditions and due to their geological importance. Computational modelling has played a crucial role in understanding the relationship between the structure and functionality of silicas and silicates including zeolites. In this study, we apply the MACE-MP-0 machine learnt interatomic potentials (ML-IP) to model the framework energies of siliceous zeolites and examine the phase transitions of silica and ZSM-5 polymorphs under high-pressure conditions. The results reproduce the known metastability of siliceous zeolites relative to {\alpha}-quartz, with energy differences between microporous and dense phases calculated by MACE-MP-0 medium ML-IP and density functional theory (DFT) methods closely aligning with experimental calorimetric data. The high-pressure simulations reveal distinct compression behaviour in the quartz, coesite, and stishovite polymorphs of silica, with coesite and stishovite showing increased stability at elevated pressures in line with experimental data. The calculated phase transition pressures from quartz to coesite (~3.5 GPa) and coesite to stishovite (~9 GPa) are close to experimental findings, demonstrating the reliability of MACE ML-IP in modelling the structural and energetic properties of silica polymorphs., Comment: 21 pages, 7 figures

Large language models (LLMs) offer powerful capabilities but also introduce significant risks. One way to mitigate these risks is through comprehensive pre-deployment evaluations using benchmarks designed to test for specific vulnerabilities. However, the rapidly expanding body of LLM benchmark literature lacks a standardized method for documenting crucial benchmark details, hindering consistent use and informed selection. BenchmarkCards addresses this gap by providing a structured framework specifically for documenting LLM benchmark properties rather than defining the entire evaluation process itself. BenchmarkCards do not prescribe how to measure or interpret benchmark results (e.g., defining ``correctness'') but instead offer a standardized way to capture and report critical characteristics like targeted risks and evaluation methodologies, including properties such as bias and fairness. This structured metadata facilitates informed benchmark selection, enabling researchers to choose appropriate benchmarks and promoting transparency and reproducibility in LLM evaluation.

Algorithmic recourse provides actions to individuals who have been adversely affected by automated decision-making and helps them achieve a desired outcome. Knowing the recourse, however, does not guarantee that users would implement it perfectly, either due to environmental variability or personal choices. Recourse generation should thus anticipate its sub-optimal or noisy implementation. While several approaches have constructed recourse that accounts for robustness to small perturbation (i.e., noisy recourse implementation), they assume an entire recourse to be implemented in a single step and thus apply one-off uniform noise to it. Such assumption is unrealistic since recourse often includes multiple sequential steps which becomes harder to implement and subject to more noise. In this work, we consider recourse under plausible noise that adapts to the local data geometry and accumulates at every step of the way. We frame this problem as a Markov Decision Process and demonstrate that the distribution of our plausible noise satisfies the Markov property. We then propose the RObust SEquential (ROSE) recourse generator to output a sequence of steps that will lead to the desired outcome even under imperfect implementation. Given our plausible modelling of sub-optimal human actions and greater recourse robustness to accumulated uncertainty, ROSE can grant users higher chances of success under low recourse costs. Empirical evaluation shows our algorithm manages the inherent trade-off between recourse robustness and costs more effectively while ensuring its low sparsity and fast computation.

Radioactive ion beams (RIB) are a key focus of current research in nuclear physics. Already long ago it was proposed that they could have applications in cancer therapy. In fact, while charged particle therapy is potentially the most effective radiotherapy technique available, it is highly susceptible to uncertainties in the beam range. RIB are well-suited for image-guided particle therapy, as isotopes that undergo \b{eta}+-decay can be precisely visualized using positron emission tomography (PET), enabling accurate real-time monitoring of the beam range. We successfully treated a mouse osteosarcoma using a radioactive 11C-ion beam. The tumor was located in the neck, in close proximity to the spinal cord, increasing the risk of radiation-induced myelopathy from even slight variations in the beam range caused by anatomical changes or incorrect calibration of the planning CT. We managed to completely control the tumor with the highest dose while minimizing toxicity. Low-grade neurological side effects were correlated to the positron activity measured in the spine. The biological washout of the activity from the tumor volume was dependent on the dose, indicating a potential component of vascular damage at high doses. This experiment marks the first instance of tumor treatment using RIB and paves the way for future clinical applications., Comment: 56 pages, 13 figures, supplements. Video supplements available on request

Clustering algorithms rely on complex optimisation processes that may be difficult to comprehend, especially for individuals who lack technical expertise. While many explainable artificial intelligence techniques exist for supervised machine learning, unsupervised learning -- and clustering in particular -- has been largely neglected. To complicate matters further, the notion of a ``true'' cluster is inherently challenging to define. These facets of unsupervised learning and its explainability make it difficult to foster trust in such methods and curtail their adoption. To address these challenges, we propose a new, model-agnostic technique for explaining clustering algorithms with counterfactual statements. Our approach relies on a novel soft-scoring method that captures the spatial information utilised by clustering models. It builds upon a state-of-the-art Bayesian counterfactual generator for supervised learning to deliver high-quality explanations. We evaluate its performance on five datasets and two clustering algorithms, and demonstrate that introducing soft scores to guide counterfactual search significantly improves the results.

The IBEX-Lo instrument on the Interstellar Boundary Explorer (IBEX) mission observes primary and secondary interstellar helium in its 4 lowest energy steps. Observations of these helium populations have been systematically analyzed and compared to simulations using the analytic full integration of neutrals model (aFINM). A systematic difference is observed between the simulations and observations of secondary helium during solar cycle (SC) 24. We show that elastic scattering of primary helium by solar wind protons, which redistributes atoms from the core of the flux distribution, provides an explanation of the observed divergence from simulations. We verify that elastic scattering forms a halo in the wings of the primary He distribution in the spin-angle direction. Correcting the simulation for the effects of elastic scattering requires an increase of the estimated density of primary helium compared to previous estimates by Ulysses/GAS. Thus, based on our analysis of IBEX observations and $\chi ^2$ minimization of simulation data that include the effects of elastic scattering, any estimation of neutral interstellar helium density at 1 AU by direct detection of the peak flux of neutral helium needs to be adjusted by $~\sim$ 10%

Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general--they are destroyed by most infinitesimal changes of the dynamical law that defines them. This fragility limits their utility especially in biological systems as their recurrent dynamics are subject to constant perturbations. We observe that the bifurcations from continuous attractors in theoretical neuroscience models display various structurally stable forms. Although their asymptotic behaviors to maintain memory are categorically distinct, their finite-time behaviors are similar. We build on the persistent manifold theory to explain the commonalities between bifurcations from and approximations of continuous attractors. Fast-slow decomposition analysis uncovers the persistent manifold that survives the seemingly destructive bifurcation. Moreover, recurrent neural networks trained on analog memory tasks display approximate continuous attractors with predicted slow manifold structures. Therefore, continuous attractors are functionally robust and remain useful as a universal analogy for understanding analog memory.

This paper proposes a new method for preventing unsafe or otherwise low quality large language model (LLM) outputs, by leveraging the stochasticity of LLMs. We propose a system whereby LLM checkers vote on the acceptability of a generated output, regenerating it if a threshold of disapproval is reached, until sufficient checkers approve. We further propose estimators for cost and failure rate, and based on those estimators and experimental data tailored to the application, we propose an algorithm that achieves a desired failure rate at the least possible cost. We demonstrate that, under these models, failure rate decreases exponentially as a function of cost when voter count and threshold are chosen according to the algorithm, and that the models reasonably estimate the actual performance of such a system in action, even with limited data., Comment: 7 pages, 2 figures

Avoiding systemic discrimination requires investigating AI models' potential to propagate stereotypes resulting from the inherent biases of training datasets. Our study investigated how text-to-image models unintentionally perpetuate non-rational beliefs regarding autism. The research protocol involved generating images based on 53 prompts aimed at visualizing concrete objects and abstract concepts related to autism across four models: DALL-E, Stable Diffusion, SDXL, and Midjourney (N=249). Expert assessment of results was performed via a framework of 10 deductive codes representing common stereotypes contested by the community regarding their presence and spatial intensity, quantified on ordinal scales and subject to statistical analysis of inter-rater reliability and size effects. The models frequently utilised controversial themes and symbols which were unevenly distributed, however, with striking homogeneity in terms of skin colour, gender, and age, with autistic individuals portrayed as engaged in solitary activities, interacting with objects rather than people, and displaying stereotypical emotional expressions such as pale, anger, or sad. Secondly we observed representational insensitivity regarding autism images despite directional prompting aimed at falsifying the above results. Additionally, DALL-E explicitly denied perpetuating stereotypes. We interpret this as ANNs mirroring the human cognitive architecture regarding the discrepancy between background and reflective knowledge, as justified by our previous research on autism-related stereotypes in humans.

Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt these models to a target domain with insufficient annotated data for effective fine-tuning. While existing domain adaptation (DA) methods propose strategies to alleviate this problem, these methods do not explicitly incorporate human-verified segmentation priors, compromising the potential of a model to produce anatomically plausible segmentations. We introduce RL4Seg, an innovative reinforcement learning framework that reduces the need to otherwise incorporate large expertly annotated datasets in the target domain, and eliminates the need for lengthy manual human review. Using a target dataset of 10,000 unannotated 2D echocardiographic images, RL4Seg not only outperforms existing state-of-the-art DA methods in accuracy but also achieves 99% anatomical validity on a subset of 220 expert-validated subjects from the target domain. Furthermore, our framework's reward network offers uncertainty estimates comparable with dedicated state-of-the-art uncertainty methods, demonstrating the utility and effectiveness of RL4Seg in overcoming domain adaptation challenges in medical image segmentation., Comment: 9 pages

The categorization of retail products is essential for the business decision-making process. It is a common practice to classify products based on their quantitative and qualitative characteristics. In this paper we use a purely data-driven approach. Our clustering of products is based exclusively on the customer behaviour. We propose a method for clustering retail products using market basket data. Our model is formulated as an optimization problem which is solved by a genetic algorithm. It is demonstrated on simulated data how our method behaves in different settings. The application using real data from a Czech drugstore company shows that our method leads to similar results in comparison with the classification by experts. The number of clusters is a parameter of our algorithm. We demonstrate that if more clusters are allowed than the original number of categories is, the method yields additional information about the structure of the product categorization.

Graph embeddings have emerged as a powerful tool for representing complex network structures in a low-dimensional space, enabling the use of efficient methods that employ the metric structure in the embedding space as a proxy for the topological structure of the data. In this paper, we explore several aspects that affect the quality of a vertex embedding of graph-structured data. To this effect, we first present a family of flexible distance functions that faithfully capture the topological distance between different vertices. Secondly, we analyze vertex embeddings as resulting from a fitted transformation of the distance matrix rather than as a direct result of optimization. Finally, we evaluate the effectiveness of our proposed embedding constructions by performing community detection on a host of benchmark datasets. The reported results are competitive with classical algorithms that operate on the entire graph while benefitting from a substantially reduced computational complexity due to the reduced dimensionality of the representations.

Despite significant progress, evaluation of explainable artificial intelligence remains elusive and challenging. In this paper we propose a fine-grained validation framework that is not overly reliant on any one facet of these sociotechnical systems, and that recognises their inherent modular structure: technical building blocks, user-facing explanatory artefacts and social communication protocols. While we concur that user studies are invaluable in assessing the quality and effectiveness of explanation presentation and delivery strategies from the explainees' perspective in a particular deployment context, the underlying explanation generation mechanisms require a separate, predominantly algorithmic validation strategy that accounts for the technical and human-centred desiderata of their (numerical) outputs. Such a comprehensive sociotechnical utility-based evaluation framework could allow to systematically reason about the properties and downstream influence of different building blocks from which explainable artificial intelligence systems are composed -- accounting for a diverse range of their engineering and social aspects -- in view of the anticipated use case., Comment: Published in Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA '24)

Should prediction models always deliver a prediction? In the pursuit of maximum predictive performance, critical considerations of reliability and fairness are often overshadowed, particularly when it comes to the role of uncertainty. Selective regression, also known as the "reject option," allows models to abstain from predictions in cases of considerable uncertainty. Initially proposed seven decades ago, approaches to selective regression have mostly focused on distribution-based proxies for measuring uncertainty, particularly conditional variance. However, this focus neglects the significant influence of model-specific biases on a model's performance. In this paper, we propose a novel approach to selective regression by leveraging conformal prediction, which provides grounded confidence measures for individual predictions based on model-specific biases. In addition, we propose a standardized evaluation framework to allow proper comparison of selective regression approaches. Via an extensive experimental approach, we demonstrate how our proposed approach, conformalized selective regression, demonstrates an advantage over multiple state-of-the-art baselines.

2D materials are emerging as promising nanomaterials for applications in energy storage and catalysis. In the wet chemical synthesis of MXenes, these 2D transition metal carbides and nitrides are terminated with a variety of functional groups, and cations such as Li+ are often used to intercalate into the structure to obtain exfoliated nanosheets. Given the various elements involved in their synthesis, it is crucial to determine the detailed chemical composition of the final product, in order to better assess and understand the relationships between composition and properties of these materials. To facilitate atom probe tomography analysis of these materials, a revised specimen preparation method is presented in this study. A colloidal Ti3C2Tz MXene solution was processed into an additive-free free-standing film and specimens were prepared using a dual beam scanning electron microscope / focused ion beam. To mechanically stabilize the fragile specimens, they were coated using an in situ sputtering technique. As various 2D material inks can be processed into such free-standing films, the presented approach is pivotal for enabling atom probe analysis of other 2D materials.