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Motivated by the recent discovery of flat bands (FBs) in breathing Kagome lattices (BKLs), we present a detailed first-principles study of the optical response of single-layer (SL) Nb$_3$Cl$_8$ using the GW-Bethe-Salpeter equation (GW-BSE) method, incorporating self-energy corrections and excitonic effects. Our findings reveal a rich spectrum of strongly bound excitons. The key results are fourfold: (i) SL Nb$_3$Cl$_8$ exhibits a dark spin-triplet Frenkel exciton ground state with binding energy substantially larger than the GW-renormalized band gap, giving rise to a negative exciton energy peak at $-0.14$ eV and indicating an excitonic Mott insulator phase potentially stable at room temperature ($k_B T = 0.025$ eV); (ii) the brightest exciton peak appears at 1.2 eV, in excellent agreement with experimental optical absorption spectra. (iii) We map the low-energy Frenkel exciton system onto a Hubbard model with spin-1 particles on a triangular lattice, resulting in frustrated spin configurations due to antiferromagnetic spin-spin exchange interaction. (iv) As the spin-triplet Frenkel excitons have electric dipoles that interact with each other via electric dipole-dipole interaction, we obtain antiferroelectric ordering, possibly stable at room temperature. Thus, we propose that Nb$_3$Cl$_8$ is a multiferroic dark spin-triplet excitonic Mott insulator., Comment: 9 pages, 5 figures

In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate or official information through the nodes) to mitigate the influence of misinformation. However, as the network size increases, the problem becomes computationally intractable. To address this, we first introduce a ranking algorithm to identify key nodes for disseminating accurate information, which facilitates the training of neural network classifiers that provide generalized solutions for the search and planning problems. Second, we mitigate the complexity of label generation, which becomes challenging as the network grows, by developing a reinforcement learning-based centralized dynamic planning framework. We analyze these NN-based planners for opinion networks governed by two dynamic propagation models. Each model incorporates both binary and continuous opinion and trust representations. Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks. Further, we observe that the reward strategies focusing on key metrics, such as the number of susceptible nodes and infection rates, outperform those prioritizing faster blocking strategies. Additionally, our findings reveal that graph convolutional network-based planners facilitate scalable centralized plans that achieve lower infection rates (higher control) across various network configurations, including Watts-Strogatz topology, varying action budgets, varying initial infected nodes, and varying degrees of infected nodes., Comment: Accepted at NeurIPS 2024

Methodology is provided towards the solution of the minimum enclosing ball problem. This problem concerns the determination of the unique spherical surface of smallest radius enclosing a given bounded set in the d-dimensional Euclidean space. Mathematical formulation and typical methods for solving this problem are presented. Also, the paper is focused on areas that are related to this problem, namely: (a) promise problems and property testing, (b) theorems for partitioning and enclosing (covering) a set, and (c) computation of the diameter of a set.

The family of 2D layered semiconductors, including transition metal chalcogenides (TMCs) of the form MX (M=Ga, In; X=S, Se, Te) exhibit exceptional nonlinear optical properties. The energetically most favorable crystal ordering for nonlinear response is the AB layer stacking, which breaks central inversion symmetry for an arbitrary number of layers, resulting in non-zero off-diagonal elements of the $\chi^{(2n')}$ tensor, $n'$ being a positive integer, for arbitrary thickness of the materials. We perform first-principles many-body calculations of bandstructures and linear and nonlinear optical responses of monolayer (ML) and bulk TMC crystals based on $GW$-Bethe-Salpeter and Kadanoff-Baym approaches in and out of equilibrium, respectively, while taking many-body band gap renormalization and excitonic effects into account. We develop a detailed analysis of the linear and nonlinear optical selection rules by means of group and representation theory. We observe the general trend that the lowest-energy excitons are dark in 2D ML TMCs whereas they are bright or mixed bright-dark in 3D bulk TMCs, which we attribute to the difference between spatially dependent screening in 2D and constant screening in 3D. In particular, we derive general formulas for the nonlinear optical response based on exciton states in semiconductor materials. We find anti-bound excitons in ML GaS, which we attribute to dominant exciton exchange interaction. We show that by choosing elements with larger mass and by reducing the detuning energy it is possible to increase the nonlinear response not only for $\chi^{(2)}$ and $\chi^{(3)}$, responsible for SHG and third harmonic generation (THG), but also in general for $\chi^{(n)}$ nonlinear response with $n>3$, giving rise to high harmonic generation (HHG) in 2D semiconductor materials., Comment: 24 pages, 16 figures

Efficient management of infrastructure systems is crucial for economic stability, sustainability, and public safety. However, infrastructure sustainment is challenging due to the vast scale of systems, stochastic deterioration of components, partial observability, and resource constraints. Decision-making strategies that rely solely on human judgment often result in suboptimal decisions over large scales and long horizons. While data-driven approaches like reinforcement learning offer promising solutions, their application has been limited by the lack of suitable simulation environments. We present InfraLib, an open-source modular and extensible framework that enables modeling and analyzing infrastructure management problems with resource constraints as sequential decision-making problems. The framework implements hierarchical, stochastic deterioration models, supports realistic partial observability, and handles practical constraints including cyclical budgets and component unavailability. InfraLib provides standardized environments for benchmarking decision-making approaches, along with tools for expert data collection and policy evaluation. Through case studies on both synthetic benchmarks and real-world road networks, we demonstrate InfraLib's ability to model diverse infrastructure management scenarios while maintaining computational efficiency at scale., Comment: Updated preprint under active review

Mapping stars and gas in nearby galaxies is fundamental for understanding their growth and the impact of their environment. This issue is addressed by comparing the stellar "edges" of galaxies $D_{\rm stellar}$, defined as the outermost diameter where in situ star formation significantly drops, with the gaseous distribution parameterized by the neutral atomic hydrogen diameter measured at 1 $M_{sun}$/pc$^2$, $D_{HI}$. By sampling a broad HI mass range $10^5 M_{sun} < M_{HI} < 10^{11} M_{sun}$, we find several dwarf galaxies with $M_{HI} < 10^9 M_{sun}$ from the field and Fornax Cluster which are distinguished by $D_{\rm stellar} >> D_{HI}$. For the cluster dwarfs, the average HI surface density near $D_{\rm stellar}$ is $\sim$0.3 $M_{sun}$/pc$^2$, reflecting the impact of quenching and outside-in gas removal from ram pressure and tidal interactions. In comparison, $D_{\rm stellar}/D_{HI}$ ranges between 0.5-2 in dwarf field galaxies, consistent with the expectations from stellar feedback. Only more massive disk galaxies in the field can thus be characterized by the common assumption that $D_{\rm stellar} \lesssim D_{HI}$. We discover a break in the $D_{\rm stellar}-M_{\rm stellar}$ relation at $m_{break} \sim 4\times10^8 M_{sun}$ that potentially differentiates the low mass regime where the influence of stellar feedback and environmental processes more prominently regulates the sizes of nearby galaxies. Our results highlight the importance of combining deep optical and HI imaging for understanding galaxy evolution., Comment: 19 pages, 10 figures, 2 tables. Key results in Figs. 4, 6, 7 and 8. Accepted for publication in The Astrophysical Journal

Combining Chandra, ALMA, EVLA, and Hubble Space Telescope archival data and newly acquired APO/DIS spectroscopy, we detect a double-lobed 17~kpc X-ray emission with plumes oriented approximately perpendicular and parallel to the galactic plane of the massive lenticular galaxy NGC\,5084 at 0.3--2.0~keV. We detect a highly inclined ($i=71.2^{+1.8\circ}_{-1.7}$), molecular circumnuclear disk ($D=304^{+10}_{-11}$ pc) in the core of the galaxy rotating (V$^{\rm (2-1) CO}_{\rm rot}=242.7^{+9.6}_{-6.4}$ km s$^{-1}$) in a direction perpendicular to that of the galactic disk, implying a total mass of $\log_{10}\left( \frac{M_{\rm BH}}{M_{\odot}} \right) = 7.66^{+0.21}_{-0.15}$ for NGC\,5084's supermassive black hole. Archival EVLA radio observations at 6 cm and 20 cm reveal two symmetric radio lobes aligned with the galactic plane, extending to a distance of $\overline{R}=4.6\pm0.6$ kpc from the core, oriented with the polar axis of the circumnuclear disk. The spectral energy distribution lacks strong emission lines in the optical range. Three formation scenarios are considered to explain these multi-wavelength archival observations: 1) AGN re-orientation caused by accretion of surrounding material, 2) AGN-driven hot gas outflow directed along the galactic minor axis, or 3) a starburst / supernovae driven outflow at the core of the galaxy. This discovery is enabled by new imaging analysis tools including \SAUNAS\ (Selective Amplification of Ultra Noisy Astronomical Signal), demonstrating the abundance of information still to be exploited in the vast and growing astronomical archives., Comment: Submitted to ApJ in May 21st 2024, in revision. 26 pages, 14 figures

Monotonic Partially Observable Markov Decision Processes (POMDPs), where the system state progressively decreases until a restorative action is performed, can be used to model sequential repair problems effectively. This paper considers the problem of solving budget-constrained multi-component monotonic POMDPs, where a finite budget limits the maximal number of restorative actions. For a large number of components, solving such a POMDP using current methods is computationally intractable due to the exponential growth in the state space with an increasing number of components. To address this challenge, we propose a two-step approach. Since the individual components of a budget-constrained multi-component monotonic POMDP are only connected via the shared budget, we first approximate the optimal budget allocation among these components using an approximation of each component POMDP's optimal value function which is obtained through a random forest model. Subsequently, we introduce an oracle-guided meta-trained Proximal Policy Optimization (PPO) algorithm to solve each of the independent budget-constrained single-component monotonic POMDPs. The oracle policy is obtained by performing value iteration on the corresponding monotonic Markov Decision Process (MDP). This two-step method provides scalability in solving truly massive multi-component monotonic POMDPs. To demonstrate the efficacy of our approach, we consider a real-world maintenance scenario that involves inspection and repair of an administrative building by a team of agents within a maintenance budget. Finally, we perform a computational complexity analysis for a varying number of components to show the scalability of the proposed approach.

Wide-separation lensed quasars (WSLQs) are a rare class of strongly lensed quasars, magnified by foreground massive galaxy clusters, with typically large magnifications of the multiple quasar images. They are a relatively unexplored opportunity for detailed study of quasar host galaxies. The current small sample of known WSLQs has a median redshift of $z\approx 2.1$, larger than most other samples of quasar host galaxies studied to date. Here, we derive precise constraints on the properties of six WSLQs and their host galaxies, using parametric surface brightness fitting, measurements of quasar emission lines, and stellar population synthesis of host galaxies in six WSLQ systems. Our results, with significant uncertainty, indicate that these six hosts are a mixture of star-forming and quiescent galaxies. To probe for co-evolution between AGNs and host galaxies, we model the offset from the `local' ($z=0$) $M_{\rm{BH}}\unicode{x2013}M_\star$ relation as a simple power-law in redshift. Accounting for selection effects, a WSLQ-based model for evolution in the $M_{\rm{BH}}\unicode{x2013}M_\star$ relation has a power-law index of $\gamma_M=-0.42\pm0.31$, consistent with no evolution. Compared to several literature samples, which mostly probe unlensed quasars at $z<2$, the WSLQ sample shows less evolution from the local relation, at $\sim 4\sigma$. We find that selection affects and choices of $M_{\rm{BH}}$ calibration are the most important systematics in these comparisons. Given that we resolve host galaxy flux confidently even from the ground in some instances, our work demonstrates that WSLQs and highly magnified AGNs are exceptional systems for future AGN$\unicode{x2013}$host co-evolution studies., Comment: Submitted to ApJ. 25 pages + 7-page appendix, 12+4 figures. Key results are shown starting with Figure 6. Comments welcome

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (conv5-FC3, ResNet and SECNN) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM/QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the conv5-FC3 network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:016

Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, traditional computational modeling and more recent machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutational effect and stability prediction. Pre-trained to predict amino acid propensity from its surrounding 3D structure, HERMES can be fine-tuned for mutational effects using our open-source code. We present a suite of HERMES models, pre-trained with different strategies, and fine-tuned to predict the stability effect of mutations. Benchmarking against other models shows that HERMES often outperforms or matches their performance in predicting mutational effect on stability, binding, and fitness. HERMES offers versatile tools for evaluating mutational effects and can be fine-tuned for specific predictive objectives., Comment: 16 pages, 8 figures

A novel material consisting of a monolayer of C$_{60}$ buckyballs with hexagonal symmetry has recently been observed experimentally, named graphullerene. In this study, we present a comprehensive \textit{ab-initio} theoretical analysis of the electronic and optical properties of both pristine and impurity-engineered monolayer graphullerene using spin-dependent density functional theory (spin-DFT). Our findings reveal that graphullerene is a direct band gap semiconductor with a band gap of approximately 1.5 eV at the $\Gamma$ point, agreeing well with experimental data. Notably, we demonstrate that by adding impurities, in particular substitutional nitrogen, substitutional boron, or adsorbent hydrogen, to graphullerene results in the formation of spin-dependent deep donor and deep acceptor levels, thereby giving rise to a variety of half-semiconductors. All the impurities exhibit a magnetic moment of approximately $\mu_B$ per impurity. This impurity engineering enables the tuning of spin-polarized exciton properties in graphullerene, with spin-dependent band gap energies ranging from 0.43 eV ($\lambda \sim$ 2.9 $\mu$m) to 1.5 eV ($\lambda \sim$ 820 nm), covering the near-infrared (NIR) and short-wavelength infrared (SWIR) regimes. Our results suggest that both pristine and impurity-engineered graphullerene have significant potential for the development of carbon-based 2D semiconductor spintronic and opto-spintronic devices., Comment: 11 pages, 7 figures

Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis., Comment: Submitted to Radiology: AI

We present SAUNAS (Selective Amplification of Ultra Noisy Astronomical Signal), a pipeline designed for detecting diffuse X-ray emission in the data obtained with the Advanced CCD Imaging Spectrometer (ACIS) of the Chandra X-ray Observatory. SAUNAS queries the available observations in the Chandra archive, performs photometric calibration, PSF (point spread function) modeling, and deconvolution, point-source removal, adaptive smoothing, and background correction. This pipeline builds on existing and well-tested software including CIAO, VorBin, and LIRA. We characterize the performance of SAUNAS through several quality performance tests, and demonstrate the broad applications and capabilities of SAUNAS using two galaxies already known to show X-ray emitting structures. SAUNAS successfully detects the 30 kpc X-ray super-wind of NGC 3079 using Chandra/ACIS datasets, matching the spatial distribution detected with more sensitive XMM-Newton observations. The analysis performed by SAUNAS reveals an extended low surface brightness source in the field of UGC 5101 in the 0.3-1.0 keV and 1.0-2.0 keV bands. This source is potentially a background galaxy cluster or a hot gas plume associated with UGC 5101. SAUNAS demonstrates its ability to recover previously undetected structures in archival data, expanding exploration into the low surface brightness X-ray universe with Chandra/ACIS., Comment: Accepted for publication in The Astrophysical Journal. 27 pages, 19 figures. The SAUNAS X-ray surface brightness and signal-to-noise maps of NGC 3079 and UGC 5101 are publicly available in Zenodo in FITS format: https://zenodo.org/records/10892485. DOI: 10.5281/zenodo.10892485. The list of Chandra datasets, obtained by the Chandra X-ray Observatory, are contained in DOI: 10.25574/cdc.225

Radio observations of strongly lensed objects are valuable as cosmological probes. Lensed radio sources have proven difficult to identify in large part due to the limited depth and angular resolution of the previous generation of radio sky surveys, and as such, only a few dozen lensed radio sources are known. In this work we present the results of a pilot study using the Very Large Array Sky Survey (VLASS) in combination with optical data to more efficiently identify lensed radio sources. We obtain high-resolution (0.2") VLA follow-up observations for 11 targets that we identify using three different techniques: i) a search for compact radio sources offset from galaxies with high lensing potential, ii) VLASS detections of known lensed galaxies, iii) VLASS detections of known lensed quasars. 5 of our targets show radio emission from the lensed images, including 100% of the lensed optical quasar systems. This work demonstrates the efficacy of combining deep and high-resolution wide-area radio and optical survey data to efficiently find lensed radio sources, and we discuss the potential impact of such an approach using next-generation surveys with the Vera C. Rubin Observatory, Euclid, and Nancy Grace Roman Space Telescope., Comment: 29 pages, 9 figures, 3 tables, submitted to AAS Journals

We estimate the Einstein-radius-enclosed total mass for 177 cluster-scale strong gravitational lenses identified by the ChicagO Optically selected Lenses Located At the Margins of Public Surveys (COOL-LAMPS) collaboration with lens redshifts ranging from $0.2 \lessapprox z \lessapprox 1.0$ using the brightest-cluster-galaxy (BCG) redshift and an observable proxy for the Einstein radius. We constrain the Einstein-radius-enclosed luminosity and stellar mass by fitting parametric spectral energy distributions to aperture photometry from the Dark Energy Camera Legacy Survey in the $g$-, $r$-, and $z$-band Dark Energy Camera filters. We find that the BCG redshift, enclosed total mass, and enclosed luminosity are strongly correlated and well described by a planar relationship in 3D space. We find that the enclosed total mass and stellar mass are correlated with a logarithmic slope of $0.500^{+0.029}_{-0.031}$, and the enclosed total mass and stellar-to-total mass fraction are correlated with a logarithmic slope of $-0.495^{+0.032}_{-0.033}$. In tandem with the small radii within which these slopes are constrained, this may suggest invariance in baryon conversion efficiency and feedback strength as a function of cluster-centric radii in galaxy clusters. Additionally, the correlations described here should have utility in ranking strong-lensing candidates in upcoming imaging surveys -- such as Rubin/Legacy Survey of Space and Time -- in which an algorithmic treatment of strong lenses will be needed due to the sheer volume of data these surveys will produce., Comment: 22 pages, 7 figures, 2 tables

Theoretical background is provided towards the mathematical foundation of the minimum enclosing ball problem. This problem concerns the determination of the unique spherical surface of smallest radius enclosing a given bounded set in the d-dimensional Euclidean space. The study of several problems that are similar or related to the minimum enclosing ball problem has received a considerable impetus from the large amount of applications of these problems in various fields of science and technology. The proposed theoretical framework is based on several enclosing (covering) and partitioning (clustering) theorems and provides among others bounds and relations between the circumradius, inradius, diameter and width of a set. These enclosing and partitioning theorems are considered as cornerstones in the field that strongly influencing developments and generalizations to other spaces and non-Euclidean geometries., Comment: arXiv admin note: text overlap with arXiv:2401.03232

The Apollonius theorem gives the length of a median of a triangle in terms of the lengths of its sides. The straightforward generalization of this theorem obtained for m-simplices in the n-dimensional Euclidean space for n greater than or equal to m is given. Based on this, generalizations of properties related to the medians of a triangle are presented. In addition, applications of the generalized Apollonius' theorem and the related to the medians results, are given for obtaining: (a) the minimal spherical surface that encloses a given simplex or a given bounded set, (b) the thickness of a simplex that it provides a measure for the quality or how well shaped a simplex is, and (c) the convergence and error estimates of the root-finding bisection method applied on simplices.