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We present a deep learning-based approach to studying dynamic clinical behavioral regimes in diverse non-randomized healthcare settings. Our proposed methodology - deep causal behavioral policy learning (DC-BPL) - uses deep learning algorithms to learn the distribution of high-dimensional clinical action paths, and identifies the causal link between these action paths and patient outcomes. Specifically, our approach: (1) identifies the causal effects of provider assignment on clinical outcomes; (2) learns the distribution of clinical actions a given provider would take given evolving patient information; (3) and combines these steps to identify the optimal provider for a given patient type and emulate that provider's care decisions. Underlying this strategy, we train a large clinical behavioral model (LCBM) on electronic health records data using a transformer architecture, and demonstrate its ability to estimate clinical behavioral policies. We propose a novel interpretation of a behavioral policy learned using the LCBM: that it is an efficient encoding of complex, often implicit, knowledge used to treat a patient. This allows us to learn a space of policies that are critical to a wide range of healthcare applications, in which the vast majority of clinical knowledge is acquired tacitly through years of practice and only a tiny fraction of information relevant to patient care is written down (e.g. in textbooks, studies or standardized guidelines).

Altermagnets recently came into the spotlight as a new class of magnetic materials, arising as a consequence of specific crystal symmetries. They are characterized by a spin-polarized electronic band structure similar to ferromagnets, but with net zero magnetization, and touted as a promising platform to host a slew of exotic properties, many of which are yet to be explored. Using spectroscopic-imaging scanning tunneling microscopy (STM) and spin-polarized STM, we discover intertwined spin and charge density waves in a layered triangular lattice altermagnet, Co-intercalated NbSe$_2$. STM topographs and differential conductance maps in the altermagnetic state show tri-directional modulations with a 2$a_0$ wave length that are robustly present and non-dispersive in a wide range of imaging biases, suggesting a charge ordering origin of the modulations. Spin-polarized STM measurements further reveal that the amplitude of the charge modulations is sensitive to the spin polarization of the tip. This observation demonstrates an additional spin modulation associated with the density wave modulations. Our work uncovers the first density wave instability involving both charge and spin degrees of freedom in an altermagnetic system, accomplished by providing elusive atomic-scale insights. Our experiments thus set the foundation for the studies of correlated electronic states in altermagnets, and motivate comprehensive explorations of intercalated quasi-2D transition metal dichalcogenides in the search for novel phenomena in altermagnets.

The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing. This poses severe challenges in efficient control, calibration, and validation of quantum states and their dynamics. Machine learning methods have emerged as powerful tools owing to their remarkable capability to learn from data, and thus have been extensively utilized for different quantum tasks. This paper reviews several significant topics related to machine learning-aided quantum estimation and control. In particular, we discuss neural networks-based learning for quantum state estimation, gradient-based learning for optimal control of quantum systems, evolutionary computation for learning control of quantum systems, machine learning for quantum robust control, and reinforcement learning for quantum control. This review provides a brief background of key concepts recurring across many of these approaches with special emphasis on neural networks, evolutionary computation, and reinforcement learning.

The ATLAS experiment at the Large Hadron Collider (LHC) is currently preparing to replace its present Inner Detector (ID) with the upgraded, all-silicon Inner Tracker (ITk) for its High-Luminosity upgrade (HL-LHC). The ITk will consist of a central pixel tracker and the outer strip tracker, consisting of about 19,000 strip detector modules. Each strip module is assembled from up to two sensors, and up to five flexes (depending on its geometry) in a series of gluing, wirebonding and quality control steps. During detector operation, modules will be cooled down to temperatures of about -35C (corresponding to the temperature of the support structures on which they will be mounted) after being initially assembled and stored at room temperature. In order to ensure compatibility with the detector's operating temperature range, modules are subjected to thermal cycling as part of their quality control process. Ten cycles between -35C and +40C are performed for each module, with full electrical characterisation tests at each high and low temperature point. As part of an investigation into the stress experienced by modules during cooling, it was observed that modules generally showed a change in module shape before and after thermal cycling. This paper presents a summary of the discovery and understanding of the observed changes, connecting them with excess module stress, as well as the resulting modifications to the module thermal cycling procedure.

Deep learning's success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and information encoding, we highlight their strengths, challenges, and potential as an alternative computational paradigm.

High-resolution colon segmentation is crucial for clinical and research applications, such as digital twins and personalized medicine. However, the leading open-source abdominal segmentation tool, TotalSegmentator, struggles with accuracy for the colon, which has a complex and variable shape, requiring time-intensive labeling. Here, we present the first fully automatic high-resolution colon segmentation method. To develop it, we first created a high resolution colon dataset using a pipeline that combines region growing with interactive machine learning to efficiently and accurately label the colon on CT colonography (CTC) images. Based on the generated dataset consisting of 435 labeled CTC images we trained an nnU-Net model for fully automatic colon segmentation. Our fully automatic model achieved an average symmetric surface distance of 0.2 mm (vs. 4.0 mm from TotalSegmentator) and a 95th percentile Hausdorff distance of 1.0 mm (vs. 18 mm from TotalSegmentator). Our segmentation accuracy substantially surpasses TotalSegmentator. We share our trained model and pipeline code, providing the first and only open-source tool for high-resolution colon segmentation. Additionally, we created a large-scale dataset of publicly available high-resolution colon labels.

Altermagnetism defies conventional classifications of collinear magnetic phases, standing apart from ferromagnetism and antiferromagnetism with its unique combination of spin-dependent symmetries, net-zero magnetization, and anomalous Hall transport. Although altermagnetic states have been realized experimentally, their integration into functional devices has been hindered by the structural rigidity and poor tunability of existing materials. First, through cobalt intercalation of the superconducting 2H-NbSe$_2$ polymorph, we induce and stabilize a robust altermagnetic phase and using both theory and experiment, we directly observe the lifting of Kramers degeneracy. Then, using ultrafast laser pulses, we demonstrate how the low temperature phase of this system can be quenched, realizing the first example of an optical altermagnetic switch. While shedding light on overlooked aspects of altermagnetism, our findings open pathways to spin-based technologies and lay a foundation for advancing the emerging field of altertronics.

The optimal strategy for deploying a treatment in a population may recommend giving all in the population that treatment. Such a strategy may not be feasible, especially in resource-limited settings. One approach for determining how to allocate a treatment in such settings is the resource-constrained optimal dynamic treatment rule (RC ODTR) SuperLearner algorithm, developed by Luedtke and van der Laan. In this paper, we describe this algorithm, offer various novel approaches for presenting the RC ODTR and its value in terms of benefit and cost, and provide practical guidance on implementing the algorithm (including software). In particular, we apply this method to the Adaptive Strategies for Preventing and Treating Lapses of Retention in HIV care (NCT02338739) trial to determine how to best allocate conditional cash transfers (CCTs) for increasing HIV care adherence given varying constraints on the proportion of people who can receive CCTs in the population, providing one of the few applied illustrations of this method and novel substantive findings. We find that there are clinical and monetary benefits to deploying CCTs to a small percent (e.g., 10\%) of the population compared to administering the care standard for all; however, results suggest that these incremental benefits are only due to the loosening of constraints, rather than a presence of treatment effect heterogeneity strong enough to drive a more efficient and effective constrained allocation approach.

Recently, it was shown how the primary steps of solvation of a single Na$^+$ ion, instantly created at the surface of a nanometer-sized droplet of liquid helium, can be followed at the atomic level [Albrectsen et al. Nature $\textbf{623}$, 319 (2023)]. This involved measuring, with femtosecond time resolution, the gradual attachment of individual He atoms to the Na$^+$ ion as well as the energy dissipated from the local region of the ion. In the current work, we provide a more comprehensive and detailed description of the experimental findings of the solvation dynamics, and present an improved Poisson-statistical analysis of the time-resovled yields of the solvation complexes, Na$^+$He$_n$. For droplets containing an average of 5200 He atoms, this analysis gives a binding rate of $1.84\pm0.09$ atoms/ps for the binding of the first five He atoms to the Na$^+$ ion. Also, thanks to accurate heoretical values for the evaporation energies of the Na$^+$He$_n$ complexes, obtained by Path Integral Monte Carlo methos using a new potential energy surface presented here for the first time, we improved the determination of the time-dependent removal of the solvation energy from the region around the sodium ion. We find that it follows Newton's law of cooling for the first 6 ps. Measurements were carried out for three different average droplet sizes, $\langle N_D\rangle = $ 9000, 5200 and 3600 helium atoms, and differences between these results are discussed., Comment: Main text: 14 pages, 8 figures. Supplementary material: 1 page, 1 figure

The estimation of all the parameters in an unknown quantum state or measurement device, commonly known as quantum state tomography (QST) and quantum detector tomography (QDT), is crucial for comprehensively characterizing and controlling quantum systems. In this paper, we introduce a framework, in two different bases, that utilizes multiple quantum processes to simultaneously identify a quantum state and a detector. We develop a closed-form algorithm for this purpose and prove that the mean squared error (MSE) scales as $O(1/N) $ for both QST and QDT, where $N $ denotes the total number of state copies. This scaling aligns with established patterns observed in previous works that addressed QST and QDT as independent tasks. Furthermore, we formulate the problem as a sum of squares (SOS) optimization problem with semialgebraic constraints, where the physical constraints of the state and detector are characterized by polynomial equalities and inequalities. The effectiveness of our proposed methods is validated through numerical examples., Comment: 36 pages, 6 figures

As the European countries strive to meet their ambitious climate goals, renewable hydrogen has emerged to aid in decarbonizing energy-intensive sectors and support the overall energy transition. To ensure that hydrogen production aligns with these goals, the European Commission has introduced criteria for additionality, temporal correlation, and geographical correlation. These criteria are designed to ensure that hydrogen production from renewable sources supports the growth of renewable energy. This study assesses the impact of these criteria on green hydrogen production, focusing on production costs and technology impacts. The European energy market is simulated up to 2048 using stochastic programming, applying these requirements exclusively to green hydrogen production without the phased-in compliance period outlined in the EU regulations. The findings show that meeting the criteria will increase expected system costs by 82 EUR billion from 2024 to 2048, largely due to the rapid shift from fossil fuels to renewable energy. The additionality requirement, which mandates the use of new renewable energy installations for electrolysis, proves to be the most expensive, but also the most effective in accelerating renewable energy adoption., Comment: arXiv admin note: text overlap with arXiv:2406.07149

This paper tests the ability of a machine vision system with a monochrome camera to differentiate colored objects. The system is designed to autonomously and continuously sort colored objects of which the user specifies the desired color(s). The system uses camera color light filters and red-green-blue (RGB) color light emitting diode (LED) lights to aid the machine vision system in recognizing part contrast. Additionally the system is controlled by a Programmable Logic Controller (PLC) which is integrated with a Robot that is used to remove parts. The parts are fed into the workcell via a conveyor belt that is controlled by the PLC. The user has the ability to select the desired acceptable colors on both the HMI and the physical pushbuttons., Comment: 12 pages, 26 figures, 23rd Annual Hawaii International Conference on Education, Ja n. 4-7, 2025, Honolulu, Hawa ii

Sequential multiple assignment randomized trials (SMARTs) have grown in popularity in recent years, and many of their study protocols propose conducting a cost effectiveness analysis of the adaptive strategies embedded within them. The cost effectiveness of these regimes is often proposed to be assessed using incremental cost effectiveness ratios (ICERs). In this paper, we present an estimation and inference procedure for such cost effectiveness measures for the embedded dynamic treatment regimes within a SMART design. In particular, we describe a targeted maximum likelihood estimator for the ICER of a SMART's embedded regimes with influence curve-based inference. We illustrate the performance of these methods using simulations. Throughout, we use as illustration a cost effectiveness analysis for the Adaptive Strategies for Preventing and Treating Lapses of Retention in HIV Care (ADAPT-R; NCT02338739) trial, presenting estimated ICERs (with inference) for embedded regimes aimed at increasing HIV care adherence. This manuscript is one of the first to present cost effectiveness analysis results from a SMART.

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal, and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein-truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5 to 37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke-Fahn-Marsden Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year, and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002, and P = 0.012)., Comment: PMID: 33150406; PMCID: PMC7719027

Temporal background information can improve causal discovery algorithms by orienting edges and identifying relevant adjustment sets. We develop the Temporal Greedy Equivalence Search (TGES) algorithm and terminology essential for score-based causal discovery with tiered background knowledge. TGES learns a restricted Markov equivalence class of directed acyclic graphs (DAGs) using observational data and tiered background knowledge. To construct TGES we formulate a scoring criterion that accounts for tiered background knowledge. We establish theoretical results for TGES, stating that the algorithm always returns a tiered maximally oriented partially directed acyclic graph (tiered MPDAG) and that this tiered MPDAG contains the true DAG in the large sample limit. We present a simulation study indicating a gain from using tiered background knowledge and an improved precision-recall trade-off compared to the temporal PC algorithm. We provide a real-world example on life-course health data.

This study explores the feasibility of using scintillation detectors for muon calorimetry through experiments conducted at CERN's T10 beamline. Results from organic scintillators with varying thicknesses and readout methods, as well as a lead-glass calorimeter, showed minimal correlation between muon energy and the peak or spread of the detected signal amplitudes. These findings highlight the influence of temperature on SiPMs and indicate a significant amount of contamination in the muon beam., Comment: 12 pages, 11 figures

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures

Multi-modal models require aligned, shared embedding spaces. However, common CLIP-based approaches need large amounts of samples and do not natively support 3D or tabular data, both of which are crucial in the medical domain. To address these issues, we revisit CLIP-style alignment by training a domain-specific 3D foundation model as an image encoder and demonstrate that modality alignment is feasible with only 62 MRI scans. Our approach is enabled by a simple embedding accumulation strategy required for training in 3D, which scales the amount of negative pairs across batches in order to stabilize training. We perform a thorough evaluation of various design choices, including the choice of backbone and loss functions, and evaluate the proposed methodology on zero-shot classification and image-retrieval tasks. While zero-shot image-retrieval remains challenging, zero-shot classification results demonstrate that the proposed approach can meaningfully align the representations of 3D MRI with tabular data., Comment: 10 pages, 2 figures. To be published in ISBI 2025

Here we present experimental evidence of the pseudo-doublet ground state of the non-Kramers compound SrTm2O4, based on specific heat, magnetic entropy and electron paramagnetic resonance. We demonstrate that the two crystallographic Tm3+ sites give rise to distinct single-ion anisotropies, and by extension, SrTm2O4 hosts two magnetic sublattices. Inelastic neutron scattering reveals low-lying dispersing crystal-field excitations, which we modelled using an effective charge model and mean field random phase approximation. The extracted magnetic exchange interactions are both antiferromagnetic and frustrated for both chains. Interchain magnetic exchange interactions are negligible. The strength of the magnetic exchange interactions in relation to the size of crystal field gaps, together with the frustration and low dimensionality, force the system to remain paramagnetic down to the lowest experimentally reachable temperature despite the pseudo-doublet nature of its ground state.

Given a family $(q_k)_k$ of polynomials, we call an open set $U$ root-sparse if the number of zeros of $q_k$ is locally uniformly bounded on $U$. We study the interplay between the individual zeros of the polynomials $q_k$ and those of the $m$th derivatives $q_k^{(m)}$, in a root-sparse open set $U$, as $k\to\infty$. More precisely, if the root distributions $\mu_k$ of $q_k$ converge weak* to some compactly supported measure $\mu$, whose potential is nowhere locally constant on a root-sparse open set $U$, then we link the roots of the $m$th derivative $q_k^{m}$, for an arbitrary $m>0$, to the roots of $q_k$ and the critical points of the potential $p_\mu$ on compact subsets of $U$. We apply this result in a polynomial dynamics setting to obtain convergence results for the roots of the $m$th derivative of iterates of a polynomial outside the filled-in Julia set. We also apply our result in the setting of extremal polynomials.

Data from the Gaia mission shows prominent phase-space spirals that are the signatures of disequilibrium in the Milky Way (MW) disc. In this work, we present a novel perspective on the phase-space spiral in angular momentum (AM) space. Using Gaia DR3, we detect a prominent AM spiral in the solar neighbourhood. We demonstrate the relation of AM to the $z-v_z$ spiral and show that we can map to this space from angular momentum through simplifying assumptions. By modelling the orbit of stars in AM, we develop a generative model for the spiral where the disc is perturbed by a bulk tilt at an earlier time. Our model successfully describes the salient features of the AM spiral in the data. Modelling the phase spiral in AM is a promising method to constrain both perturbation and MW potential parameters. Our AM framework simplifies the interpretation of the spiral and offers a robust approach to modelling disequilibrium in the MW disc using all six dimensions of phase space simultaneously., Comment: 10 pages, 5 figures

Multi-agent systems are increasingly applied in space missions, including distributed space systems, resilient constellations, and autonomous rendezvous and docking operations. A critical emerging application is collaborative spacecraft servicing, which encompasses on-orbit maintenance, space debris removal, and swarm-based satellite repositioning. These missions involve servicing spacecraft interacting with malfunctioning or defunct spacecraft under challenging conditions, such as limited state information, measurement inaccuracies, and erratic target behaviors. Existing approaches often rely on assumptions of full state knowledge or single-integrator dynamics, which are impractical for real-world applications involving second-order spacecraft dynamics. This work addresses these challenges by developing a distributed state estimation and tracking framework that requires only relative position measurements and operates under partial state information. A novel $\rho$-filter is introduced to reconstruct unknown states using locally available information, and a Lyapunov-based deep neural network adaptive controller is developed that adaptively compensates for uncertainties stemming from unknown spacecraft dynamics. To ensure the collaborative spacecraft regulation problem is well-posed, a trackability condition is defined. A Lyapunov-based stability analysis is provided to ensure exponential convergence of errors in state estimation and spacecraft regulation to a neighborhood of the origin under the trackability condition. The developed method eliminates the need for expensive velocity sensors or extensive pre-training, offering a practical and robust solution for spacecraft servicing in complex, dynamic environments., Comment: 24 pages, 4 Figures, Journal

Ultrasound localization microscopy (ULM) enables microvascular imaging at spatial resolutions beyond the acoustic diffraction limit, offering significant clinical potentials. However, ULM performance relies heavily on microbubble (MB) signal sparsity, the number of detected MBs, and signal-to-noise ratio (SNR), all of which vary in clinical scenarios involving bolus MB injections. These sources of variations underscore the need to optimize MB dosage, data acquisition timing, and imaging settings in order to standardize and optimize ULM of microvasculature. This pilot study investigated temporal changes in MB signals during bolus injections in both pig and human models to optimize data acquisition for clinical ULM. Quantitative indices were developed to evaluate MB signal quality, guiding selection of acquisition timing that balances the MB localization quality and adequate MB counts. The effects of transmitted voltage and dosage were also explored. In the pig model, a relatively short window (approximately 10 seconds) for optimal acquisition was identified during the rapid wash-out phase, highlighting the need for real-time MB signal monitoring during data acquisition. The slower wash-out phase in humans allowed for a more flexible imaging window of 1-2 minutes, while trade-offs were observed between localization quality and MB density (or acquisition length) at different wash-out phase timings. Guided by these findings, robust ULM imaging was achieved in both pig and human kidneys using a short period of data acquisition, demonstrating its feasibility in clinical practice. This study provides insights into optimizing data acquisition for consistent and reproducible ULM, paving the way for its standardization and broader clinical applications., Comment: 33 pages, 9 figures

We introduce a general framework to study the local dynamics of near-parabolic maps using the meromorphic $1$-form introduced by X.~Buff. As a sample application of this setup, we prove the following tameness result on invariant curves of near-parabolic maps: Let $g(z)=\lambda z+O(z^2)$ have a non-degenerate parabolic fixed point at $0$ with multiplier $\lambda$ a primitive $q$th root of unity, and let $\gamma: \, ]-\infty,0] \to {\mathbb D}(0,r)$ be a $g^{\circ q}$-invariant curve landing at $0$ in the sense that $g^{\circ q}(\gamma(t))=\gamma(t+1)$ and $\lim_{t \to -\infty} \gamma(t)=0$. Take a sequence $g_n(z)=\lambda_n z+O(z^2)$ with $|\lambda_n|\neq 1$ such that $g_n \to g$ uniformly on ${\mathbb D}(0,r)$ and suppose each $g_n$ admits a $g_n^{\circ q}$-invariant curve $\gamma_n: \, ]-\infty,0] \to {\mathbb C}$ such that $\gamma_n \to \gamma$ uniformly on the fundamental segment $[-1,0]$. If $\lambda_n^q \to 1$ non-tangentially, then $\gamma_n$ lands at a repelling periodic point near $0$, and $\gamma_n \to \gamma$ uniformly on $]-\infty,0]$. In the special case of polynomial maps, this proves Hausdorff continuity of external rays of a given periodic angle when the associated multipliers approach a root of unity non-tangentially., Comment: 32 pages, 8 figures

This paper investigates the linear response of a series of spheroidal stellar clusters, the Kuzmin-Kutuzov St\"ackel family, which exhibit a continuous range of flattening and rotation, extending from an isochrone sphere to a Toomre disk. The method successfully replicates the growing modes previously identified in published $N$-body simulations. It relies on the efficiency of the matrix method to quantify systematically the effects of rotation and flattening on the eigenmodes of the galaxy. We identify two types of bi-symmetric instabilities for the flatter models - the so-called bending and bar-growing modes - the latter of which persists even for very round models. As anticipated, in its least unstable configurations, the system becomes flatter as its rotational speed increases. More realistic equilibria will be required to achieve a better match to the main sequence of fast-slow rotators. The corresponding code is made public., Comment: 20 pages, 15 figures. Submitted to ApJ

We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach that uses a generative model to learn a joint distribution over discrete and continuous design variables to sample new hybrid designs. In contrast to standard decoupled approaches, in which the discrete and continuous variables are optimized separately, our joint optimization approach uses fewer objective function evaluations, is robust against non-differentiable objectives, and learns from prior samples to guide the search, leading to significant improvement in performance and sample efficiency. Our experiments on a diverse set of optimization tasks demonstrate that the advantages of DisCo-DSO become increasingly evident as the complexity of the problem increases. In particular, we illustrate DisCo-DSO's superiority over the state-of-the-art methods for interpretable reinforcement learning with decision trees., Comment: Accepted at AAAI-25

New proposals for causal discovery algorithms are typically evaluated using simulations and a few select real data examples with known data generating mechanisms. However, there does not exist a general guideline for how such evaluation studies should be designed, and therefore, comparing results across different studies can be difficult. In this article, we propose a common evaluation baseline by posing the question: Are we doing better than random guessing? For the task of graph skeleton estimation, we derive exact distributional results under random guessing for the expected behavior of a range of typical causal discovery evaluation metrics (including precision and recall). We show that these metrics can achieve very large values under random guessing in certain scenarios, and hence warn against using them without also reporting negative control results, i.e., performance under random guessing. We also propose an exact test of overall skeleton fit, and showcase its use on a real data application. Finally, we propose a general pipeline for using random controls beyond the skeleton estimation task, and apply it both in a simulated example and a real data application.

We prove that a classifier with a Barron-regular decision boundary can be approximated with a rate of high polynomial degree by ReLU neural networks with three hidden layers when a margin condition is assumed. In particular, for strong margin conditions, high-dimensional discontinuous classifiers can be approximated with a rate that is typically only achievable when approximating a low-dimensional smooth function. We demonstrate how these expression rate bounds imply fast-rate learning bounds that are close to $n^{-1}$ where $n$ is the number of samples. In addition, we carry out comprehensive numerical experimentation on binary classification problems with various margins. We study three different dimensions, with the highest dimensional problem corresponding to images from the MNIST data set.

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.

We study an extended Proca model with one scalar field and one massive vector field in one space and one time dimensions. We construct the soliton solution and subsequently compute the vacuum polarization energy (VPE) which is the leading quantum correction to the classical energy of the soliton. For this calculation we adopt the spectral methods approach which heavily relies on the analytic properties of the Jost function. This function is extracted from the interaction of the quantum fluctuations with a background potential generated by the soliton. Particularly we explore eventual non-analytical components that may be induced by mass gaps and the unconventional normalization for the longitudinal component of the vector field fluctuations. By numerical simulation we verify that these obstacles do actually not arise and that the real and imaginary momentum formulations of the VPE yield equal results. The Born approximation to the Jost function is crucial when implementing standard renormalization conditions. In this context we solve problems arising from the Born approximation being imaginary for real momenta associated with energies in the mass gap., Comment: 13 pages, content matches journal version

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research., Comment: Updating author information, the submission remains largely unchanged. 98 pages total

We present a novel technique using Fourier series and Laguerre polynomials to represent morphological features of disc galaxies. To demonstrate the utility of this technique, we study the evolution of asymmetry in a sample of disc galaxies drawn from the Extended Groth Strip and imaged by the JWST Cosmic Evolution Early Release Science Survey as well as archival HST observations. We measure disc asymmetry as the amplitude of the of the m = 1 Fourier harmonic for galaxies within redshift ranges of 1 < z < 4. We show that when viewed in shorter rest frame wavelengths, disc galaxies have a higher asymmetry as the flux is dominated by star forming regions. We find generally low asymmetry at rest frame infrared wavelengths, where our metric tracks asymmetry in morphological features such as bars and spiral arms. We show that higher mass galaxies have lower asymmetry and vice versa. Higher asymmetry in lower mass galaxies comes from lower mass galaxies (typically) having higher star formation rates. We measure the relation between disc galaxy asymmetry and redshift and find no conclusive relationship between them. We demonstrate the utility of the Fourier-Laguerre technique for recovering physically informative asymmetry measurements as compared to rotational asymmetry measurements. We also release the software pipeline and quantitative analysis for each galaxy., Comment: 12 pages and 11 figures. Under review in MNRAS and comments welcome

The dissociative photoionization of \textit{tert}-butyl methyl methacrylate, a monomer unit found in many ESCAP resists, was investigated in a gas phase photoelectron photoion coincidence experiment employing extreme ultraviolet (EUV) synchrotron radiation at 13.5 nm. It was found that the interaction of EUV photons with the molecules leads almost exclusively to dissociation. However, the ionization can also directly deprotect the ester function, thus inducing the solubility switch wanted in a resist film. These results serve as a building block to reconstruct the full picture of the mechanism in widely used chemically amplified resist thin films, provide a knob to tailor more performant resist materials, and will aid interpreting advanced ultrafast time-resolved experiments.

Cervical cancer remains a major worldwide health issue, with early identification and risk assessment playing critical roles in effective preventive interventions. This paper presents the Cervix-AID-Net model for cervical precancer risk classification. The study designs and evaluates the proposed Cervix-AID-Net model based on patients colposcopy images. The model comprises a Convolutional Block Attention Module (CBAM) and convolutional layers that extract interpretable and representative features of colposcopic images to distinguish high-risk and low-risk cervical precancer. In addition, the proposed Cervix-AID-Net model integrates four explainable techniques, namely gradient class activation maps, Local Interpretable Model-agnostic Explanations, CartoonX, and pixel rate distortion explanation based on output feature maps and input features. The evaluation using holdout and ten-fold cross-validation techniques yielded a classification accuracy of 99.33\% and 99.81\%. The analysis revealed that CartoonX provides meticulous explanations for the decision of the Cervix-AID-Net model due to its ability to provide the relevant piece-wise smooth part of the image. The effect of Gaussian noise and blur on the input shows that the performance remains unchanged up to Gaussian noise of 3\% and blur of 10\%, while the performance reduces thereafter. A comparison study of the proposed model's performance compared to other deep learning approaches highlights the Cervix-AID-Net model's potential as a supplemental tool for increasing the effectiveness of cervical precancer risk assessment. The proposed method, which incorporates the CBAM and explainable artificial integration, has the potential to influence cervical cancer prevention and early detection, improving patient outcomes and lowering the worldwide burden of this preventable disease., Comment: 19 pages, 9 figure, and 7 tables

Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify the confidence of model inferences. However, chained models pose an additional challenge for uncertainty quantification, especially when input uncertainties need to be propagated. In such cases, a fully uncertainty-aware chain of models is required, where each step accepts a probability distribution over the input space, and produces a probability distribution over the output space. In this work, we present a case study for adapting a single model within an existing chain, designed for reconstruction within neutrino-Argon interactions, developed for neutrino oscillation experiments such as MicroBooNE, ICARUS, and the future DUNE experiment. We test the performance of an input uncertainty-enabled model against an uncertainty-blinded model using a method for generating synthetic noise. By comparing these two, we assess the increase in inference quality achieved by exposing models to upstream uncertainty estimates.

In this paper, an $\mathscr{H}_2$ norm-based model reduction method for linear quantum systems is presented, which can obtain a physically realizable model with a reduced order for closely approximating the original system. The model reduction problem is described as an optimization problem, whose objective is taken as an $\mathscr{H}_2$ norm of the difference between the transfer function of the original system and that of the reduced one. Different from classical model reduction problems, physical realizability conditions for guaranteeing that the reduced-order system is also a quantum system should be taken as nonlinear constraints in the optimization. To solve the optimization problem with such nonlinear constraints, we employ a matrix inequality approach to transform nonlinear inequality constraints into readily solvable linear matrix inequalities (LMIs) and nonlinear equality constraints, so that the optimization problem can be solved by a lifting variables approach. We emphasize that different from existing work, which only introduces a criterion to evaluate the performance after model reduction, we guide our method to obtain an optimal reduced model with respect to the $\mathscr{H}_2$ norm. In addition, the above approach for model reduction is extended to passive linear quantum systems. Finally, examples of active and passive linear quantum systems validate the efficacy of the proposed method., Comment: 13 pages,3 figures