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Discussions regarding the dual use of foundation models and the risks they pose have overwhelmingly focused on a narrow set of use cases and national security directives-in particular, how AI may enable the efficient construction of a class of systems referred to as CBRN: chemical, biological, radiological and nuclear weapons. The overwhelming focus on these hypothetical and narrow themes has occluded a much-needed conversation regarding present uses of AI for military systems, specifically ISTAR: intelligence, surveillance, target acquisition, and reconnaissance. These are the uses most grounded in actual deployments of AI that pose life-or-death stakes for civilians, where misuses and failures pose geopolitical consequences and military escalations. This is particularly underscored by novel proliferation risks specific to the widespread availability of commercial models and the lack of effective approaches that reliably prevent them from contributing to ISTAR capabilities. In this paper, we outline the significant national security concerns emanating from current and envisioned uses of commercial foundation models outside of CBRN contexts, and critique the narrowing of the policy debate that has resulted from a CBRN focus (e.g. compute thresholds, model weight release). We demonstrate that the inability to prevent personally identifiable information from contributing to ISTAR capabilities within commercial foundation models may lead to the use and proliferation of military AI technologies by adversaries. We also show how the usage of foundation models within military settings inherently expands the attack vectors of military systems and the defense infrastructures they interface with. We conclude that in order to secure military systems and limit the proliferation of AI armaments, it may be necessary to insulate military AI systems and personal data from commercial foundation models.

The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented as a double-differential cross section as a function of the direct observables of the final-state muon kinematics. Results are also presented as a single-differential cross section as a function of the derived square of the four-momentum transfer, $Q^{2}$, and as a function of the derived neutrino energy. The data correspond to an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the neutrino mode of the NuMI beam, with a narrow band of neutrino energies peaked at 1.8 GeV. The analysis provides a sample of neutrino-nucleus interactions with an enhanced fraction of quasi-elastic and two-particle-two-hole (2p2h) interactions. This enhancement allows quantitative comparisons with various nuclear models. We find strong disagreement between data and theory-based models in various regions of the muon kinematic phase space, especially in the forward muon direction., Comment: 20 pages, 12 figures

High-spin states in molecular systems hold significant interest for a wide range of applications ranging from optoelectronics to quantum information and singlet fission (SF). Quintet and triplet states play crucial roles, particularly in SF systems, necessitating a precise monitoring and control of their spin dynamics. Spin states in intramolecular SF (iSF) are of particular interest, but tuning these systems to control triplet multiplication pathways has not been extensively studied. Additionally, whilst studies in this context focus on participation of triplet pathways leading to photoluminescence, emission pathways via quintet states remain largely unexplored. Here, we employ a set of unique spin-sensitive techniques to investigate high-spin state formation and emission in dimers and trimers comprising multiple diphenylhexatriene (DPH) units. We demonstrate the formation of pure quintet states in all these oligomers, with optical emission via quintet states dominating delayed fluorescence up to room temperature. For triplet formation, we distinguish between SF and ISC pathways, identifying the trimer Me-(DPH)$_3$ as the only oligomer exhibiting exclusively the desired SF pathways. Conversely, linear (DPH)$_3$ and (DPH)$_2$ show additional or exclusive triplet pathways via ISC. Our comprehensive analysis provides a detailed investigation into high-spin state formation, control, and emission in intramolecular singlet fission systems.

Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean muon-neutrino energy of 1.86 GeV. The measurements are based on an estimated 995,760 muon-neutrino CC interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole reactions is identified by the cross-section excess relative to predictions for neutrino-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2- hole processes are rated by chi-square comparisons of the predicted-versus-measured muon-neutrino CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based Val`encia and SuSAv2 2p2h models., Comment: 20 pages, 14 figures

Nuclear magnetism underpins areas such as medicine in magnetic resonance imaging (MRI). Hyperpolarization of nuclei enhances the quantity and quality of information that can be determined from these techniques by increasing their signal to noise ratios by orders of magnitude. However, some of these hyperpolarization techniques rely on the use of low to ultralow magnetic fields (ULF) (nTs-mTs), where empirical data on how nuclear magnetism behaves is lacking. The broadband character and ultrasensitive field sensitivity of superconducting quantum interference devices (SQUID) allow for probing nuclear magnetism at these fields, where other magnetometers, such as Faraday coils and flux gates do not. To this end, we designed a hyperpolarization reactor to hyperpolarize [1-$^{13}$C]pyruvate with the technique, signal amplification by reversible exchange in shield enables alignment transfer to heteronuclei (SABRE-SHEATH). Afterwards, we were able to characterize the field sensitivity of our setup by simulating the filled reactor in relation to its placement in our ultralow noise, ULF MRI setup. Using the results of the simulations, we determined that our hyperpolarization setup results in a $^{13}$C polarization of 0.4%, a signal enhancement of ~100 000 000 versus the predicted thermal equilibrium signal at earth field (~50 $\mu$T). This results in a $^{13}$C signal of 7.44$\pm$0.91 pT, which with our ultralow noise setup, opens the possibility for the first direct detection of nuclear magnetism without system perturbation., Comment: 5 pages, 7 figures, ASC 2024, submitted to IEEE TAS

Artificial Intelligence (AI), with its multiplier effect and wide applications in multiple areas, could potentially be an important application of quantum computing. Since modern AI systems are often built on neural networks, the design of quantum neural networks becomes a key challenge in integrating quantum computing into AI. To provide a more fine-grained characterisation of the impact of quantum components on the performance of neural networks, we propose a framework where classical neural network layers are gradually replaced by quantum layers that have the same type of input and output while keeping the flow of information between layers unchanged, different from most current research in quantum neural network, which favours an end-to-end quantum model. We start with a simple three-layer classical neural network without any normalisation layers or activation functions, and gradually change the classical layers to the corresponding quantum versions. We conduct numerical experiments on image classification datasets such as the MNIST, FashionMNIST and CIFAR-10 datasets to demonstrate the change of performance brought by the systematic introduction of quantum components. Through this framework, our research sheds new light on the design of future quantum neural network models where it could be more favourable to search for methods and frameworks that harness the advantages from both the classical and quantum worlds., Comment: 50 pages (including Appendix), many figures, accepted as a poster on QTML2024. Code available at https://github.com/peiyong-addwater/Let-The-Quantum-Creep-In

Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sources. However, there are many parameters to optimize in just the retrieval system alone. This paper presents an ablation study exploring how different embedding models and pooling methods affect information retrieval for the clinical domain. Methods: Evaluating on three retrieval tasks on two electronic health record (EHR) data sources, we compared seven models, including medical- and general-domain models, specialized encoder embedding models, and off-the-shelf decoder LLMs. We also examine the choice of embedding pooling strategy for each model, independently on the query and the text to retrieve. Results: We found that the choice of embedding model significantly impacts retrieval performance, with BGE, a comparatively small general-domain model, consistently outperforming all others, including medical-specific models. However, our findings also revealed substantial variability across datasets and query text phrasings. We also determined the best pooling methods for each of these models to guide future design of retrieval systems. Discussion: The choice of embedding model, pooling strategy, and query formulation can significantly impact retrieval performance and the performance of these models on other public benchmarks does not necessarily transfer to new domains. Further studies such as this one are vital for guiding empirically-grounded development of retrieval frameworks, such as in the context of RAG, for the clinical domain.

The introduction of generative pre-trained models, like GPT-4, has introduced a phenomenon known as prompt engineering, whereby model users repeatedly write and revise prompts while trying to achieve a task. Using these AI models for intelligent features in software applications require using APIs that are controlled through developer-written prompts. These prompts have powered AI experiences in popular software products, potentially reaching millions of users. Despite the growing impact of prompt-powered software, little is known about its development process and its relationship to programming. In this work, we argue that some forms of prompts are programs, and that the development of prompts is a distinct phenomenon in programming. We refer to this phenomenon as prompt programming. To this end, we develop an understanding of prompt programming using Straussian grounded theory through interviews with 20 developers engaged in prompt development across a variety of contexts, models, domains, and prompt complexities. Through this study, we contribute 14 observations about prompt programming. For example, rather than building mental models of code, prompt programmers develop mental models of the FM's behavior on the prompt and its unique qualities by interacting with the model. While prior research has shown that experts have well-formed mental models, we find that prompt programmers who have developed dozens of prompts, each with many iterations, still struggle to develop reliable mental models. This contributes to a rapid and unsystematic development process. Taken together, our observations indicate that prompt programming is significantly different from traditional software development, motivating the creation of tools to support prompt programming. Our findings have implications for software engineering practitioners, educators, and researchers.

Coronary artery calcium (CAC) is highly predictive of cardiovascular events. While millions of chest CT scans are performed annually in the United States, CAC is not routinely quantified from scans done for non-cardiac purposes. A deep learning algorithm was developed using 446 expert segmentations to automatically quantify CAC on non-contrast, non-gated CT scans (AI-CAC). Our study differs from prior works as we leverage imaging data across the Veterans Affairs national healthcare system, from 98 medical centers, capturing extensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC performance on non-gated scans was compared against clinical standard ECG-gated CAC scoring. Non-gated AI-CAC differentiated zero vs. non-zero and less than 100 vs. 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and 87.3% (F1 0.89), respectively, in 795 patients with paired gated scans within a year of a non-gated CT scan. Non-gated AI-CAC was predictive of 10-year all-cause mortality (CAC 0 vs. >400 group: 25.4% vs. 60.2%, Cox HR 3.49, p < 0.005), and composite first-time stroke, MI, or death (CAC 0 vs. >400 group: 33.5% vs. 63.8%, Cox HR 3.00, p < 0.005). In a screening dataset of 8,052 patients with low-dose lung cancer-screening CTs (LDCT), 3,091/8,052 (38.4%) individuals had AI-CAC >400. Four cardiologists qualitatively reviewed LDCT images from a random sample of >400 AI-CAC patients and verified that 527/531 (99.2%) would benefit from lipid-lowering therapy. To the best of our knowledge, this is the first non-gated CT CAC algorithm developed across a national healthcare system, on multiple imaging protocols, without filtering intra-cardiac hardware, and compared against a strong gated CT reference. We report superior performance relative to previous CAC algorithms evaluated against paired gated scans that included patients with intra-cardiac hardware.

This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains --like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings., Comment: Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024

We present new Galactic reddening maps of the high Galactic latitude sky using DESI imaging and spectroscopy. We directly measure the reddening of 2.6 million stars by comparing the observed stellar colors in $g-r$ and $r-z$ from DESI imaging with the synthetic colors derived from DESI spectra from the first two years of the survey. The reddening in the two colors is on average consistent with the \cite{fitzpatrick_correcting_1999} extinction curve with $R_\mathrm{V}=3.1$. We find that our reddening maps differ significantly from the commonly used \cite{schlegel_maps_1998} (SFD) reddening map (by up to 80 mmag in $E(B-V)$), and we attribute most of this difference to systematic errors in the SFD map. To validate the reddening map, we select a galaxy sample with extinction correction based on our reddening map, and this yields significantly better uniformity than the SFD extinction correction. Finally, we discuss the potential systematic errors in the DESI reddening measurements, including the photometric calibration errors that are the limiting factor on our accuracy. The $E(g-r)$ and $E(g-r)$ maps presented in this work, and for convenience their corresponding $E(B-V)$ maps with SFD calibration, are publicly available., Comment: Submitted to the Open Journal of Astrophysics. Associated data files: https://data.desi.lbl.gov/public/papers/mws/desi_dust/y2/v1/maps/

Brightness mode (B-mode) ultrasound is a common imaging modality in the clinical assessment of several cardiovascular diseases. The utility of ultrasound-based functional indices such as the ejection fraction (EF) and stroke volume (SV) is widely described in diagnosing advanced-stage cardiovascular diseases. Additionally, structural indices obtained through the analysis of cardiac motion have been found to be important in the early-stage assessment of structural heart diseases, such as hypertrophic cardiomyopathy and myocardial infarction. Estimating heterogeneous variations in cardiac motion through B-mode ultrasound imaging is a crucial component of patient care. Despite the benefits of such imaging techniques, motion estimation algorithms are susceptible to variability between vendors due to the lack of benchmark motion quantities. In contrast, finite element (FE) simulations of cardiac biomechanics leverage well-established constitutive models of the myocardium to ensure reproducibility. In this study, we developed a methodology to create synthetic B-mode ultrasound images from FE simulations. The proposed methodology provides a detailed representation of displacements and strains under complex mouse-specific loading protocols of the LV. A comparison between the synthetic images and FE simulations revealed qualitative similarity in displacement patterns, thereby yielding benchmark quantities to improve the reproducibility of motion estimation algorithms. Thus, the study provides a methodology to create an extensive repository of images describing complex motion patterns to facilitate the enhanced reproducibility of cardiac motion analysis.

We introduce AIRduct, a new array-based intermediate representation designed to support generating efficient code for interactive programs employing multiple cryptographic mechanisms. AIRduct is intended as an IR for the Viaduct compiler, which can synthesize secure, distributed programs with an extensible suite of cryptography. Therefore, AIRduct supports an extensible variety of cryptographic mechanisms, including MPC and ZKP.

Learned language-conditioned robot policies often struggle to effectively adapt to new real-world tasks even when pre-trained across a diverse set of instructions. We propose a novel approach for few-shot adaptation to unseen tasks that exploits the semantic understanding of task decomposition provided by vision-language models (VLMs). Our method, Policy Adaptation via Language Optimization (PALO), combines a handful of demonstrations of a task with proposed language decompositions sampled from a VLM to quickly enable rapid nonparametric adaptation, avoiding the need for a larger fine-tuning dataset. We evaluate PALO on extensive real-world experiments consisting of challenging unseen, long-horizon robot manipulation tasks. We find that PALO is able of consistently complete long-horizon, multi-tier tasks in the real world, outperforming state of the art pre-trained generalist policies, and methods that have access to the same demonstrations., Comment: 27 pages, 14 figures

Measuring the growth of structure is a powerful probe for studying the dark sector, especially in light of the $\sigma_8$ tension between primary CMB anisotropy and low-redshift surveys. This paper provides a new measurement of the amplitude of the matter power spectrum, $\sigma_8$, using galaxy-galaxy and galaxy-CMB lensing power spectra of Dark Energy Spectroscopic Instrument Legacy Imaging Surveys Emission-Line Galaxies and the $\textit{Planck}$ 2018 CMB lensing map. We create an ELG catalog composed of $27$ million galaxies and with a purity of $85\%$, covering a redshift range $0 < z < 3$, with $z_{\rm mean} = 1.09$. We implement several novel systematic corrections, such as jointly modeling the contribution of imaging systematics and photometric redshift uncertainties to the covariance matrix. We also study the impacts of various dust maps on cosmological parameter inference. We measure the cross-power spectra over $f_{\rm sky} = 0.25$ with a signal-to-background ratio of up to $ 30\sigma$. We find that the choice of dust maps to account for imaging systematics in estimating the ELG overdensity field has a significant impact on the final estimated values of $\sigma_8$ and $\Omega_{\rm M}$, with far-infrared emission-based dust maps preferring $\sigma_8$ to be as low as $0.702 \pm 0.030$, and stellar-reddening-based dust maps preferring as high as $0.719 \pm 0.030$. The highest preferred value is at $\sim 3 \sigma$ tension with the $\textit{Planck}$ primary anisotropy results. These findings indicate a need for tomographic analyses at high redshifts and joint modeling of systematics., Comment: 50 pages, 24 figures (figure data can be obtained at https://zenodo.org/records/13381499)

Solar-type stars have been observed to flare at optical wavelengths to energies much higher than observed for the Sun. To date, no counterparts have been observed at longer wavelengths. We have searched the the VLA Sky Survey (VLASS) for radio emission associated with a sample of 150 single, solar-type stars previously been observed to exhibit superflares in the Transiting Exoplanet Survey Satellite (TESS). Counterparts to six of these stars were present in VLASS as transient or highly variable radio sources. One of the stars is detected in all three epochs, exhibiting an unprecedented level of apparently persistent radio emission. The engine for this radio emission is unclear, but may be related to accretion, a binary companion, or the presence of large-scale magnetic field. Two stars show radio emission with >50 circular polarization fraction, indicating a coherent emission process likely being present. We find that the six VLASS-detected stars tend to have higher flare rates and higher flare energies of our TESS sample. This, in addition to the VLASS-detected stars adhering to the Gudel-Benz relation, suggest that the radio emission may be directly associated with superflares. These results confirm that the superflare phenomenon on solar-type stars extends to radio wavelengths, in this instance tracing particle acceleration. These data provide the first window on the luminosity function of radio superflares for solar-type stars and highlights the need for coordinated, multi-wavelength monitoring of such stars to fully illustrate the stellar flare-particle relation., Comment: 20 pages, 6 figures, 3 tables

The Dark Energy Spectroscopic Instrument (DESI) Peculiar Velocity Survey aims to measure the peculiar velocities of early and late type galaxies within the DESI footprint using both the Fundamental Plane and Tully-Fisher relations. Direct measurements of peculiar velocities can significantly improve constraints on the growth rate of structure, reducing uncertainty by a factor of approximately 2.5 at redshift 0.1 compared to the DESI Bright Galaxy Survey's redshift space distortion measurements alone. We assess the quality of stellar velocity dispersion measurements from DESI spectroscopic data. These measurements, along with photometric data from the Legacy Survey, establish the Fundamental Plane relation and determine distances and peculiar velocities of early-type galaxies. During Survey Validation, we obtain spectra for 6698 unique early-type galaxies, up to a photometric redshift of 0.15. 64\% of observed galaxies (4267) have relative velocity dispersion errors below 10\%. This percentage increases to 75\% if we restrict our sample to galaxies with spectroscopic redshifts below 0.1. We use the measured central velocity dispersion, along with photometry from the DESI Legacy Imaging Surveys, to fit the Fundamental Plane parameters using a 3D Gaussian maximum likelihood algorithm that accounts for measurement uncertainties and selection cuts. In addition, we conduct zero-point calibration using the absolute distance measurements to the Coma cluster, leading to a value of the Hubble constant, $H_0 = 76.05 \pm 0.35$(statistical) $\pm 0.49$(systematic FP) $\pm 4.86$(statistical due to calibration) $\mathrm{km \ s^{-1} Mpc^{-1}}$. This $H_0$ value is within $2\sigma$ of Planck Cosmic Microwave Background results and within $1\sigma$, of other low redshift distance indicator-based measurements., Comment: 18 pages, 9 figures, 2 tables. Submitted for publication in MNRAS

We use 1-4" (300-1200 au) resolution 12CO(2-1) data from the MASSES (Mass Assembly of Stellar Systems and their Evolution with the SMA) project to measure the projected opening angles of 46 protostellar outflows in the Perseus Molecular Cloud, 37 of which are measured with sufficiently high confidence to use in further analysis. We find that there is a statistically significant difference in the distributions of outflow opening angles for Class 0 and Class I outflows, with a distinct lack of both wide-angle Class 0 outflows and highly collimated Class I outflows. Synthesizing our results with several previous studies, we find that outflows widen with age through the Class 0 stage but do not continue to widen in the Class I stage. The maximum projected opening angle reached is approximately 90 degrees +/- 20 degrees, with the transition between widening and remaining constant occurring near the boundary between the Class 0 and Class I phases of evolution. While the volume fractions occupied by these outflows are no more than a few tens of percent of the total core volume, at most, recent theoretical work suggests outflows may still be capable of playing a central role in setting the low star formation efficiencies of 25%-50% observed on core scales., Comment: 35 pages, accepted by MNRAS

Self-supervision has the potential to transform reinforcement learning (RL), paralleling the breakthroughs it has enabled in other areas of machine learning. While self-supervised learning in other domains aims to find patterns in a fixed dataset, self-supervised goal-conditioned reinforcement learning (GCRL) agents discover new behaviors by learning from the goals achieved during unstructured interaction with the environment. However, these methods have failed to see similar success, both due to a lack of data from slow environments as well as a lack of stable algorithms. We take a step toward addressing both of these issues by releasing a high-performance codebase and benchmark JaxGCRL for self-supervised GCRL, enabling researchers to train agents for millions of environment steps in minutes on a single GPU. The key to this performance is a combination of GPU-accelerated environments and a stable, batched version of the contrastive reinforcement learning algorithm, based on an infoNCE objective, that effectively makes use of this increased data throughput. With this approach, we provide a foundation for future research in self-supervised GCRL, enabling researchers to quickly iterate on new ideas and evaluate them in a diverse set of challenging environments. Website + Code: https://github.com/MichalBortkiewicz/JaxGCRL

During relativistic magnetic reconnection, antiparallel magnetic fields undergo a rapid change in topology, releasing a large amount of energy in the form of non-thermal particle acceleration. This work explores the application of mesh refinement to 2D reconnection simulations to efficiently model the ineherent disparity in length-scales. We have systematically investigated the effects of mesh refinement and determined necessary modifications to the algorithm required to mitigate non-physical artifacts at the coarse-fine interface. We have used the ultrahigh-order Pseudo-Spectral Analytical Time-Domain (PSATD) Maxwell solver to analyze how its use can mitigate the numerical dispersion that occurs with the finite-difference time-domain (FDTD) (or ``Yee'') method. Absorbing layers are introduced at the coarse-fine interface to eliminate spurious effects that occur with mesh refinement. We also study how damping the electromagnetic fields and current density in the absorbing layer can help prevent the non-physical accumulation of charge and current density at the coarse-fine interface. Using a mesh refinement ratio of 8 for two-dimensional magnetic reconnection simulations, we obtained good agreement with the high resolution baseline simulation, using only 36% of the macroparticles and 71% of the node-hours needed for the baseline. The methods presented here are especially applicable to 3D systems where higher memory savings are expected than in 2D, enabling comprehensive, computationally efficient 3D reconnection studies in the future.

The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical data pivotal in clinical contexts, into LLM paradigms has not been thoroughly explored. In this study, we examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record (EHR) data. We compare the performance of these embeddings with that of raw numerical EHR data when used as feature inputs to traditional machine learning (ML) algorithms that excel at tabular data learning, such as eXtreme Gradient Boosting. We focus on instruction-tuned LLMs in a zero-shot setting to represent abnormal physiological data and evaluating their utilities as feature extractors to enhance ML classifiers for predicting diagnoses, length of stay, and mortality. Furthermore, we examine prompt engineering techniques on zero-shot and few-shot LLM embeddings to measure their impact comprehensively. Although findings suggest the raw data features still prevails in medical ML tasks, zero-shot LLM embeddings demonstrate competitive results, suggesting a promising avenue for future research in medical applications., Comment: Accepted to Findings of EMNLP 2024

Galaxy scaling relations provide insights into the processes that drive galaxy evolution. The extension of these scaling relations into the dwarf galaxy regime is of particular interest. This is because dwarf galaxies represent a crucial stage in galaxy evolution, and understanding them could also shed light on their role in reionising the early Universe. There is currently no consensus on the processes that dominate the evolution of dwarfs. In this work we constrain the atomic gas sequence (stellar mass vs. atomic gas fraction) and mass-metallicity relation (stellar mass vs. gas phase metallicity) from dwarf ($10^{6.5}$ $\textrm{M}_{\odot}$) to massive ($10^{11.5}$ $\textrm{M}_{\odot}$) galaxies in the local Universe. The combined optical and 21-cm spectroscopic observations of the DESI and ALFALFA surveys allow us to simultaneously constrain both scaling relations. We find a slope change of the atomic gas sequence at a stellar mass of $\sim 10^{9} ~\textrm{M}_{\odot}$. We also find that the shape and scatter of the atomic gas sequence and mass-metallicity relation are strongly linked for both dwarfs and more massive galaxies. Consequently, the low mass slope change of the atomic gas sequence is imprinted onto the mass-metallicity relation of dwarf galaxies. The mass scale of the measured slope change is consistent with a predicted escape velocity threshold below which low mass galaxies experience significant supernova-driven gas loss, as well as with a reduction in cold gas accretion onto more massive galaxies., Comment: 16 pages, 10 figures, submitted to MNRAS

Single-molecule magnets (SMMs) with chemically tailorable properties are potential building blocks for quantum computing, high-density magnetic memory, and spintronics.1 2 3,4 These applications require isolated or few molecules on substrates, but studies of SMMs have mainly focused on bulk crystals. Moreover, fabrication of SMM-based devices and electrical detection of the SMM magnetic state are still coveted milestones that have so far been achieved mainly for double-decker rare-earth phthalocyanines at temperatures below 1 K.5-8 Here we demonstrate electrical detection of magnetization switching for a modification of the archetypal SMM Mn12, up to 70 K, based on the supramolecular spin valve effect5 with graphene quantum dots9. Notably, the exchange interaction between the molecules and the graphene, as well as the dot-mediated intermolecular interaction, can be directly extracted from the electrical response, opening the way to an effective characterization of the quantum properties of different types of SMMs in a wide temperature range.

We present a study connecting the physical properties of protostellar envelopes to the morphology of the envelope-scale magnetic field. We use the ALMA polarization observations of 55 young prtostars at 0.87 mm on $\sim400-3000$ au scales from the {\em B}-field Orion Protostellar Survey (BOPS) to infer the envelope-scale magnetic field and both dust and gas emission on comparable scales to measure the envelope properties. We find that the protostellar envelopes with compact polarized dust emission tend to have lower envelope masses, than the sources with more extended envelopes. We also find that protostars showing hourglass-field morphologies tend to have lower velocity dispersions in their envelopes, whereas systems with spiral-field morphologies have higher velocity dispersion. Combining with the disk properties taken from the Orion VLA/ALMA Nascent Disk and Multiplicity (VANDAM) survey, we connect envelope properties to fragmentation. Our results suggest that envelope mass may not correlate with fragmentation, whereas turbulence appears to promote fragmentation. On the other hand, we find that fragmentation is suppressed in systems with pinched magnetic fields, suggesting that the magnetic field play a role on providing additional support against gravitational collapse, and the formation of an hourglass-like field may coincide with enhanced magnetic braking that removes angular momentum and hinders the formation of embedded disks. Nevertheless, significant misalignment between magnetic field and outflow axes tends to reduce magnetic braking, leading to the formation of larger disks., Comment: 18 pages, 6 figures, submitted to ApJ

When applying quantum computing to machine learning tasks, one of the first considerations is the design of the quantum machine learning model itself. Conventionally, the design of quantum machine learning algorithms relies on the ``quantisation" of classical learning algorithms, such as using quantum linear algebra to implement important subroutines of classical algorithms, if not the entire algorithm, seeking to achieve quantum advantage through possible run-time accelerations brought by quantum computing. However, recent research has started questioning whether quantum advantage via speedup is the right goal for quantum machine learning [1]. Research also has been undertaken to exploit properties that are unique to quantum systems, such as quantum contextuality, to better design quantum machine learning models [2]. In this paper, we take an alternative approach by incorporating the heuristics and empirical evidences from the design of classical deep learning algorithms to the design of quantum neural networks. We first construct a model based on the data reuploading circuit [3] with the quantum Hamiltonian data embedding unitary [4]. Through numerical experiments on images datasets, including the famous MNIST and FashionMNIST datasets, we demonstrate that our model outperforms the quantum convolutional neural network (QCNN)[5] by a large margin (up to over 40% on MNIST test set). Based on the model design process and numerical results, we then laid out six principles for designing quantum machine learning models, especially quantum neural networks., Comment: 11 figures, 31 pages. Code available on https://github.com/peiyong-addwater/HamEmbedding. Author affiliation updated for v2. Acknowledgements and funding information added for v2

The formulation of the S-matrix as a path integral with specified asymptotic boundary conditions naturally leads to the realization of a Carrollian partition function defined on the boundary of Minkowski space. This partition function, specified at past and future null infinity in the case of massless particles, generates Carrollian correlation functions that encode the S-matrix. We explore this connection, including the realization of symmetries, soft theorems arising from large gauge transformations, and the correspondence with standard momentum space amplitudes. This framework is also well-suited for embedding the Minkowski space S-matrix into the AdS/CFT duality in the large radius limit. In particular, we identify the AdS and Carrollian partition functions through a simple map between their respective asymptotic data, establishing a direct correspondence between the actions of symmetries on both sides. Our approach thus provides a coherent framework that ties together various topics extensively studied in recent and past literature., Comment: 59 pages; v2 corrected typos, added discussion of flat space Witten diagrams in section 3

(abridged)Photometric redshifts for AGN (galaxies hosting an accreting supermassive black hole in their center) are notoriously challenging and currently better computed via SED fitting, assuming that deep photometry for many wavelengths is available. However, for AGN detected all-sky, the photometry is limited and provided by different projects. This makes the task of homogenising the data challenging and is a dramatic drawback for the millions of AGN that wide surveys like SRG/eROSITA will detect. This work aims to compute reliable photometric redshifts for X-ray-detected AGN using only one dataset that covers a large area: the 10th Data Release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures, thus avoids issues related to the cross-calibration of surveys. We present the results from CircleZ, a machine-learning algorithm based on a Fully Connected Neural Network. CircleZ uses training sample of 14,000 X-ray-detected AGN and utilizes multi-aperture photometry. The accuracy and the fraction of outliers reached in a test sample of 2913 AGN are 0.067 and 11.6%, respectively. The results are comparable to or better than those obtained previously for the same field but with much less effort. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching comparable accuracy as in eFEDS when limiting the magnitude of the counterparts with respect to the depth of LS10. The method applies to fainter samples of AGN using deeper optical data from future surveys (e.g., LSST, Euclid), granted LS10-like information on the light distribution beyond a morphological type is provided. With the paper, we release an updated version of the photometric redshifts (including errors and probability distribution function) for eROSITA/eFEDS., Comment: A&A, paper accepted

Hierarchical segmentation entails creating segmentations at varying levels of granularity. We introduce the first hierarchical semantic segmentation dataset with subpart annotations for natural images, which we call SPIN (SubPartImageNet). We also introduce two novel evaluation metrics to evaluate how well algorithms capture spatial and semantic relationships across hierarchical levels. We benchmark modern models across three different tasks and analyze their strengths and weaknesses across objects, parts, and subparts. To facilitate community-wide progress, we publicly release our dataset at https://joshmyersdean.github.io/spin/index.html., Comment: Accepted at ECCV 2024

Lung injuries, such as ventilator-induced lung injury and radiation-induced lung injury, can lead to heterogeneous alterations in the biomechanical behavior of the lungs. While imaging methods, e.g., X-ray and static computed tomography (CT), can point to regional alterations in lung structure between healthy and diseased tissue, they fall short of delineating timewise kinematic variations between the former and the latter. Image registration has gained recent interest as a tool to estimate the displacement experienced by the lungs during respiration via regional deformation metrics such as volumetric expansion and distortion. However, successful image registration commonly relies on a temporal series of image stacks with small displacements in the lungs across succeeding image stacks, which remains limited in static imaging. In this study, we have presented a finite element (FE) method to estimate strains from static images acquired at the end-expiration (EE) and end-inspiration (EI) timepoints, i.e., images with a large deformation between the two distant timepoints. Physiologically realistic loads were applied to the geometry obtained at EE to deform this geometry to match the geometry obtained at EI. The results indicated that the simulation could minimize the error between the two geometries. Using four-dimensional (4D) dynamic CT in a rat, the strain at an isolated transverse plane estimated by our method showed sufficient agreement with that estimated through non-rigid image registration that used all the timepoints. Through the proposed method, we can estimate the lung deformation at any timepoint between EE and EI. The proposed method offers a tool to estimate timewise regional deformation in the lungs using only static images acquired at EE and EI., Comment: 4 pages, 4 figures, accepted in the IEEE EMBC 2024 conference

Securing smart contracts remains a fundamental challenge. At its core, it is about building software that is secure in composition with untrusted code, a challenge that extends far beyond blockchains. We introduce SCIF, a language for building smart contracts that are compositionally secure. SCIF is based on the fundamentally compositional principle of secure information flow, but extends this core mechanism to include protection against reentrancy attacks, confused deputy attacks, and improper error handling, even in the presence of malicious contracts that do not follow SCIF's rules. SCIF supports a rich ecosystem of interacting principals with partial trust through its mechanisms for dynamic trust management. SCIF has been implemented as a compiler to Solidity. We describe the SCIF language, including its static checking rules and runtime. Finally, we implement several applications with intricate security reasoning, showing how SCIF supports building complex smart contracts securely and gives programmer accurate diagnostics about potential security bugs.

Hawking's black hole area theorem provides a geometric realization of the second law of thermodynamics and constrains gravitational processes. In this work we explore a one-parameter extension of this constraint formulated in terms of the monotonicity properties of R\'enyi entropies. We focus on black hole mergers in asymptotically AdS space and determine new restrictions which these R\'enyi second laws impose on the final state. We evaluate the entropic inequalities starting from the thermodynamic ensembles description of black hole geometries, and find that for many situations they set more stringent bounds than those imposed by the area increase theorem., Comment: 50 pages, 12 figures

The anisotropic optical absorption edge of $\beta$-Ga$_2$O$_3$ follows a modified Beer-Lambert law having two effective absorption coefficients. The absorption coefficient of linearly polarized light reduces to the least absorbing direction beyond a critical penetration depth, which itself depends on polarization and wavelength. To understand this behavior, a Stokes vector analysis is performed to track the polarization state as a function of depth. The weakening of the absorption coefficient is associated with a gradual shift of linear polarization to the least absorbing crystallographic direction in the plane, which is along the a-exciton within the (010) plane or along the b-exciton in the (001) plane. We show that strong linear dichroism near the optical absorption edge causes this shift in $\beta$-Ga$_2$O$_3$, which arises from the anisotropy and spectral splitting of the physical absorbers i.e., excitons. The linear polarization shift is accompanied by a variation in the ellipticity due to the birefringence of $\beta$-Ga$_2$O$_3$. Analysis of the phase relationship between the incoming electric field to that at a certain depth reveals the phase speed as an effective refractive index, which varies along different crystallographic directions. The critical penetration depth is shown to be correlated with the depth at which the ellipticity is maximal. Thus, the anisotropic Beer-Lambert law arises from the interplay of both the dichroic and birefringent properties of $\beta$-Ga$_2$O$_3$., Comment: 11 pages, 5 figures

We report an estimation of the injected mass composition of ultra-high energy cosmic rays (UHECRs) at energies higher than 10 EeV. The composition is inferred from an energy-dependent sky distribution of UHECR events observed by the Telescope Array surface detector by comparing it to the Large Scale Structure of the local Universe. In the case of negligible extra-galactic magnetic fields the results are consistent with a relatively heavy injected composition at E ~ 10 EeV that becomes lighter up to E ~ 100 EeV, while the composition at E > 100 EeV is very heavy. The latter is true even in the presence of highest experimentally allowed extra-galactic magnetic fields, while the composition at lower energies can be light if a strong EGMF is present. The effect of the uncertainty in the galactic magnetic field on these results is subdominant., Comment: 8 pages, 3 figures, accepted for publication in PRL

We use a new method to estimate the injected mass composition of ultrahigh cosmic rays (UHECRs) at energies higher than 10 EeV. The method is based on comparison of the energy-dependent distribution of cosmic ray arrival directions as measured by the Telescope Array experiment (TA) with that calculated in a given putative model of UHECR under the assumption that sources trace the large-scale structure (LSS) of the Universe. As we report in the companion letter, the TA data show large deflections with respect to the LSS which can be explained, assuming small extra-galactic magnetic fields (EGMF), by an intermediate composition changing to a heavy one (iron) in the highest energy bin. Here we show that these results are robust to uncertainties in UHECR injection spectra, the energy scale of the experiment and galactic magnetic fields (GMF). The assumption of weak EGMF, however, strongly affects this interpretation at all but the highest energies E > 100 EeV, where the remarkable isotropy of the data implies a heavy injected composition even in the case of strong EGMF. This result also holds if UHECR sources are as rare as $2 \times 10^{-5}$ Mpc$^{-3}$, that is the conservative lower limit for the source number density., Comment: 18 pages, 11 figures, accepted for publication in PRD

With the number of supernovae observed expected to drastically increase thanks to large-scale surveys like the Dark Energy Spectroscopic Instrument (DESI), it is necessary that the tools we use to classify these objects keep up with this increase. We previously created Supernova Tagging and Classification (STag) to address this problem by employing machine learning techniques alongside logistic regression in order to assign 'tags' to spectra based on spectral features. STag II is a continuation of this work, which now makes use of model supernova spectra combined with real DESI spectra in order to train STag to better deal with realistic data. We also make use of the rlap score as a trustworthiness cut, making for a more robust and accurate supernova classifier than before., Comment: 27 pages, 7 figures. The STag code is available at https://github.com/wdavison909/STag

Temporal distances lie at the heart of many algorithms for planning, control, and reinforcement learning that involve reaching goals, allowing one to estimate the transit time between two states. However, prior attempts to define such temporal distances in stochastic settings have been stymied by an important limitation: these prior approaches do not satisfy the triangle inequality. This is not merely a definitional concern, but translates to an inability to generalize and find shortest paths. In this paper, we build on prior work in contrastive learning and quasimetrics to show how successor features learned by contrastive learning (after a change of variables) form a temporal distance that does satisfy the triangle inequality, even in stochastic settings. Importantly, this temporal distance is computationally efficient to estimate, even in high-dimensional and stochastic settings. Experiments in controlled settings and benchmark suites demonstrate that an RL algorithm based on these new temporal distances exhibits combinatorial generalization (i.e., "stitching") and can sometimes learn more quickly than prior methods, including those based on quasimetrics., Comment: Proceedings of the 41st International Conference on Machine Learning (ICML 2024)

ZnS is a brittle material but shows extraordinary plasticity during mechanical tests performed in complete darkness. This phenomenon is known as the photoplastic effect, whose underlying mechanisms have long been unclear. We study the impact of light, via photoexcited charge carriers, on the dislocation core structure and mobility using first-principles calculations. We calculate the core structure and the charge-dependent Peierls barriers of the glide set of Shockley partial dislocations in ZnS. Our findings reveal that locally charged dislocations capture excess carriers in the system, leading to core reconstructions that alter the Peierls barrier, resulting in higher barriers and lower mobility for these dislocations. This altered and asymmetric mobility, depending on dislocation character (edge or mixed) and local stoichiometry (Zn or S rich), is responsible for the brittle behavior of ZnS under light exposure and will be reversed in complete darkness.

We present several algorithms for evaluating point containment in constructive solid geometry (CSG) trees with unbounded primitives. Three algorithms are presented based on postfix, prefix, and infix notations of the CSG binary expression tree. We show that prefix and infix notations enable short-circuiting logic, which reduces the number of primitives that must be checked during point containment. To evaluate the performance of the algorithms, each algorithm was implemented in the OpenMC Monte Carlo particle transport code, which relies on CSG to represent solid bodies through which subatomic particles travel. Two sets of tests were carried out. First, the execution time to generate a high-resolution rasterized image of a 2D slice of a detailed CSG model of the ITER tokamak was measured. Use of both prefix and infix notations offered significant speedup over the postfix notation that has traditionally been used in particle transport codes, with infix resulting in a 6$\times$ reduction in execution time relative to postfix. We then measured the execution time of neutron transport simulations of the same ITER model using each of the algorithms. The results and performance improvements reveal the same trends as for the rasterization test, with a 4.59$\times$ overall speedup using the infix notation relative to the original postfix notation in OpenMC.

We present ALMA Band 7 molecular line observations of the protostars within the VLA 1623 system. We map C$^{17}$O (3 - 2) in the circumbinary disk around VLA 1623A and the outflow cavity walls of the collimated outflow. We further detect red-shifted and blue-shifted velocity gradients in the circumstellar disks around VLA 1623B and VLA 1623W that are consistent with Keplerian rotation. We use the radiative transfer modeling code, pdspy, and simple flared disk models to measure stellar masses of $0.27 \pm 0.03$ M$_\odot$, $1.9^{+0.3}_{-0.2}$ M$_\odot$, and $0.64 \pm 0.06$ M$_\odot$ for the VLA 1623A binary, VLA 1623B, and VLA 1623W, respectively. These results represent the strongest constraints on stellar mass for both VLA 1623B and VLA 1623W, and the first measurement of mass for all stellar components using the same tracer and methodology. We use these masses to discuss the relationship between the young stellar objects (YSOs) in the VLA 1623 system. We find that VLA 1623W is unlikely to be an ejected YSO, as has been previously proposed. While we cannot rule out that VLA 1623W is a unrelated YSO, we propose that it is a true companion star to the VLA 1623A/B system and that the these stars formed in situ through turbulent fragmentation and have had only some dynamical interactions since their inception., Comment: Accepted to A&A; 16 pages, 12 figures

We present a careful calibration of the exciton fraction of polaritons in high-$Q$ ($\sim 300,000$), long-lifetime ($\sim 300$ ps), GaAs/AlGaAs microcavities.This is a crucial parameter for many-body theories which include the polariton-polariton interactions.It is much harder to establish this number in high-$Q$ structures compared to low-$Q$ structures, because the upper polariton is nearly invisible in high-$Q$ cavities.We present a combination of photoluminescence, photoluminescence excitation, and reflectivity measurements to highly constrain the fit model, and compare the results of this model to the results from low-$Q$ structures.We present a fitted curve of exciton fraction as a function of the lower polariton energy for multiple samples which have been used in prior experiments., Comment: 16 pages, 11 figures

We introduce the shape module of the Python package Geomstats to analyze shapes of objects represented as landmarks, curves and surfaces across fields of natural sciences and engineering. The shape module first implements widely used shape spaces, such as the Kendall shape space, as well as elastic spaces of discrete curves and surfaces. The shape module further implements the abstract mathematical structures of group actions, fiber bundles, quotient spaces and associated Riemannian metrics which allow users to build their own shape spaces. The Riemannian geometry tools enable users to compare, average, interpolate between shapes inside a given shape space. These essential operations can then be leveraged to perform statistics and machine learning on shape data. We present the object-oriented implementation of the shape module along with illustrative examples and show how it can be used to perform statistics and machine learning on shape spaces.