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Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the large number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys will potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an $r$-band $5\sigma$ magnitude limit of $24.8$ and survey footprint of $1347$ deg$^2$, it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an ``organised random'' (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOM) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this ``SOM+HC'' method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the $27\sigma$ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal setup suggested by the mock tests. Our software is open-source for future usage., Comment: 29 pages, 27 figures, 4 tables

We investigate the effect of primordial non-Gaussianities on halo number counts using N-body simulations with different values of $f_{\rm NL}^{\rm loc}$. We show how current theoretical models fail to adequately describe the non-Gaussian mass function of halos identified with different overdensity thresholds, $\Delta_{\rm b}$. We explain how these discrepancies are related to a variation in the density profile of dark matter halos, finding that the internal steepness (i.e. the compactness) of halos depends on the value of $f_{\rm NL}^{\rm loc}$. We then parametrize these deviations in halo number counts with a factor $\kappa(\Delta_{\rm b})$ that modifies the linear density threshold for collapse according to the halo identification threshold used, defined with respect to the Universe background density. We rely on a second-degree polynomial to describe $\kappa$ and employ a Bayesian analysis to determine the coefficients of this polynomial. In addition, we verify the independence of the latter on the sign and absolute value of $f_{\rm NL}^{\rm loc}$. Finally, we show how this re-parametrization prevents the extraction of biased constraints on $f_{\rm NL}^{\rm loc}$, correcting for large systematic errors especially in the case of halos identified with high density thresholds. This improvement is crucial in the perspective of deriving cosmological constraints with the non-Gaussian mass function from real data, as different mass definitions can be employed depending on the properties of the survey., Comment: 28 pages, 8 figures, to be submitted to JCAP

The pepenaactivity is part of the informal sector, characterized by precariousness and social invisibility; however, it is a key element in the recycling production chain, providing an economic income for many people. In the city of Chihuahua, this activity is carried out on a significant scale. Therefore, this study analyzes and dignifies the recycling of urban solid waste from the perspective of scavengers, also explaining their relationship with other social actors at the city's final disposal site. The study starts from the premise that waste picking is, in the current era of global environmental crisis, an effective way to conserve resources and reduce environmental impacts. Methodologically, participatory action research was used as a strategy to raise awareness among scavengers about the importance of their work and to present their community organization as an example to other urban waste picking groups in different locations. Finally, we found issues in these groups such as informality, lack of legal support,specific health risks, and, more broadly, the absence of public policies to recognize this activity as valuable in addressing the global environmental crisis, Comment: in Spanish language

This paper studies moving 180-degree N\'eel walls in ferromagnetic thin films under the reduced model for the in-plane magnetization proposed by Capella, Melcher and Otto [5], in the case when a sufficiently weak external magnetic field is applied. It is shown that the linearization around the moving N\'eel wall's phase determines a spectral problem that is a relatively bounded perturbation of the linearization around the static N\'eel wall, which is the solution when the external magnetic field is set to zero and which is spectrally stable. Uniform resolvent-type estimates for the linearized operator around the static wall are established in order to prove the spectral stability of the moving wall upon application of perturbation theory for linear operators. The spectral analysis is the basis to prove, in turn, both the decaying properties of the generated semigroup and the nonlinear stability of the moving N\'eel wall under small perturbations, in the case of a sufficiently weak external magnetic field. The stability of the static N\'eel wall, which was established in a companion paper [4], plays a key role to obtain the main result., Comment: 40 pages, 4 figures

Small business owners stand to benefit from generative AI technologies due to limited resources, yet they must navigate increasing legal and ethical risks. In this paper, we interview 11 entrepreneurs and support personnel to investigate existing practices of how entrepreneurs integrate generative AI technologies into their business workflows. Specifically, we build on scholarship in HCI which emphasizes the role of small, offline networks in supporting entrepreneurs' technology maintenance. We detail how entrepreneurs resourcefully leveraged their local networks to discover new use cases of generative AI (e.g., by sharing accounts), assuage heightened techno-anxieties (e.g., by recruiting trusted confidants), overcome barriers to sustained use (e.g., by receiving wrap-around support), and establish boundaries of use. Further, we suggest how generative AI platforms may be redesigned to better support entrepreneurs, such as by taking into account the benefits and tensions of use in a social context., Comment: 6 pages, 2 figures, to be published in Companion of the 2024 Computer-Supported Cooperative Work and Social Computing (CSCW Companion '24), November 9-13, 2024, San Jose, Costa Rica

The spatial distribution of galaxy clusters is a valuable probe for inferring fundamental cosmological parameters. We measured the clustering properties of dark matter haloes from the \textsc{Pinocchio} simulations, in the redshift range $0.2 < z < 1.0$ and with virial masses $M_\mathrm{vir} > 10^{14} M_\odot \, h^{-1}$, which reproduce the expected mass selection of galaxy cluster samples. The past-light cones we analysed have an angular size of 60 degrees, which approximately corresponds to a quarter of the sky. We adopted a linear power spectrum model, accounting for nonlinear corrections at the baryon acoustic oscillations scale, to perform a comparative study between 3D and 2D tomographic clustering. For this purpose, we modelled the multipoles of the 3D two-point correlation function, $\xi(r)$, the angular correlation function, $w(\theta)$, and the angular power spectrum, $C_\ell$. We considered observational effects such as redshift-space distortions, produced by the peculiar velocities of tracers, and redshift errors. We found that photometric redshift errors have a more severe consequence on the 3D than on the 2D clustering, as they affect only the radial separation between haloes and not the angular one, with a relevant impact on the 3D multipoles. Using a Bayesian analysis, we explored the posterior distributions of the considered probes with different tomographic strategies, in the $\Omega_m-\sigma_8$ plane, focusing on the summary parameter $S_8\equiv \sigma_8\sqrt{\Omega_m/0.3}$. Our results show that in the presence of large photometric errors the 2D clustering can provide competitive cosmological constraints with respect to the full 3D clustering statistics, and can be successfully applied to analyse the galaxy cluster catalogues from the ongoing and forthcoming Stage-III and Stage-IV photometric redshift surveys., Comment: 24 pages, 16 figures, submitted to Astronomy & Astrophysics

By studying the total fusion and breakup cross-sections in the interaction of the neutron-halo $^{11}{\rm Be}$ projectile on the lead target $^{208}$Pb, it is shown that, even for the neutron-halo projectile, the breakup channel remains the most dominant reaction channel at sub-barrier energies, following a characteristic behavior that was also previously verified for the case of the proton-halo projectile $^8{\rm B}$. This feature is found to emanate from the enhancement of the breakup cross-section, due to the continuum-continuum couplings coming exclusively from its Coulomb component. We further speculate that the enhancement of the Coulomb breakup cross-section at sub-barrier incident energies by the continuum-continuum couplings could be associated with the projectile breaking up on the outgoing trajectory, provided these couplings can be proven to delay the breakup process., Comment: 8 pages, 7 figures

We present the results of a set of cosmological N-body simulations with standard $\Lambda$CDM cosmology but characterized by a scale-dependent primordial non-Gaussianity of the local type featuring a power-law dependence of the $f_{\rm NL}^{\rm loc}(k)$ at large scales followed by a saturation to a constant value at smaller scales where non-linear growth leads to the formation of collapsed cosmic structures. Such models are built to ensure consistency with current Cosmic Microwave Background bounds on primordial non-Gaussianity yet allowing for large effects of the non-Gaussian statistics on the properties of non-linear structure formation. We show the impact of such scale-dependent non-Gaussian scenarios on a wide range of properties of the resulting cosmic structures, such as the non-linear matter power spectrum, the halo and sub-halo mass functions, the concentration-mass relation, the halo and void density profiles, and we highlight for the first time that some of these models might mimic the effects of Warm Dark Matter for several of such observables, Comment: 21 pages, 9 figures, 2 tables; to be submitted to JCAP

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose $200+$ concrete research questions.

This work aims at assessing the impact of DM self-interactions on the properties of galaxy clusters. In particular, the goal is to study the angular dependence of the cross section by testing rare (large angle scattering) and frequent (small angle scattering) SIDM models with velocity-dependent cross sections. We re-simulate six galaxy cluster zoom-in initial conditions with a dark matter only run and with a full-physics setup simulations that includes a self-consistent treatment of baryon physics. We test the dark matter only setup and the full physics setup with either collisionless cold dark matter, rare self-interacting dark matter, and frequent self-interacting dark matter models. We then study their matter density profiles as well as their subhalo population. Our dark matter only SIDM simlations agree with theoretical models, and when baryons are included in simulations, our SIDM models substantially increase the central density of galaxy cluster cores compared to full-physics simulations using collisionless dark matter. SIDM subhalo suppression in full-physics simulations is milder compared to the one found in dark matter only simulations, because of the cuspier baryionic potential that prevent subhalo disruption. Moreover SIDM with small-angle scattering significantly suppress a larger number of subhaloes compared to large angle scattering SIDM models. Additionally, SIDM models generate a broader range of subhalo concentration values, including a tail of more diffuse subhaloes in the outskirts of galaxy clusters and a population of more compact subhaloes in the cluster cores., Comment: 14 pages, 15 figures, abstract abridged to fit arXiv character limit, submitted to Astronomy and Astrophysics

A visually rich document (VRD) utilizes visual features along with linguistic cues to disseminate information. Training a custom extractor that identifies named entities from a document requires a large number of instances of the target document type annotated at textual and visual modalities. This is an expensive bottleneck in enterprise scenarios, where we want to train custom extractors for thousands of different document types in a scalable way. Pre-training an extractor model on unlabeled instances of the target document type, followed by a fine-tuning step on human-labeled instances does not work in these scenarios, as it surpasses the maximum allowable training time allocated for the extractor. We address this scenario by proposing a Noise-Aware Training method or NAT in this paper. Instead of acquiring expensive human-labeled documents, NAT utilizes weakly labeled documents to train an extractor in a scalable way. To avoid degradation in the model's quality due to noisy, weakly labeled samples, NAT estimates the confidence of each training sample and incorporates it as uncertainty measure during training. We train multiple state-of-the-art extractor models using NAT. Experiments on a number of publicly available and in-house datasets show that NAT-trained models are not only robust in performance -- it outperforms a transfer-learning baseline by up to 6% in terms of macro-F1 score, but it is also more label-efficient -- it reduces the amount of human-effort required to obtain comparable performance by up to 73%.

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

Multiband superconductors have featured one of the main challenges to achieve a comprehensive understanding of unconventional superconductivity. Here, the multiband character is studied separately as orbital and sublattice degrees of freedom, as they have different effects for the superconducting and magnetic or charge orders. We build on the framework of the matrix random-phase approximation (RPA), which accounts for the RPA Feynman diagrams and also vertex corrections, to treat the electron-electron interactions in an off-site degenerate Hubbard model. As a result, systems without a sublattice degree of freedom tend to be dominated by spin fluctuations, while systems with multiple sublattice sites and orbitals have the charge fluctuations favored. Finally, we explicitly demonstrate that the known suppression of the superconducting pairing strength $\lambda$ by spin fluctuations from repulsive interactions at zero momentum transfer $\boldsymbol{q}$ is countered by the finite-$\boldsymbol{q}$ pairing, which always improves $\lambda$., Comment: 10 + 6 pages, 2 figures

Context. Weak gravitational lensing offers a powerful method to investigate the projected matter density distribution within galaxy clusters, granting crucial insights into the broader landscape of dark matter on cluster scales. Aims. In this study, we make use of the large photometric galaxy cluster data set derived from the publicly available Third Data Release of the Kilo-Degree Survey, along with the associated shear signal. Our primary objective is to model the peculiar sharp transition in the cluster profile slope, that is what is commonly referred to as the splashback radius. The data set under scrutiny includes 6962 galaxy clusters, selected by AMICO - an optimised detection algorithm of galaxy clusters - on the KiDS-DR3 data, in the redshift range of 0.1 < z < 0.6, all observed at a signal-to-noise ratio greater than 3.5. Methods. Employing a comprehensive Bayesian analysis, we model the stacked excess surface mass density distribution of the clusters. We adopt a model from recent results on numerical simulations that capture the dynamics of both orbiting and infalling materials, separated by the region where the density profile slope undergoes a pronounced deepening. Results. We find that the adopted profile successfully characterizes the cluster masses, consistent with previous works, and models the deepening of the slope of the density profiles measured with weak-lensing data up to the outskirts. Moreover, we measure the splashback radius of galaxy clusters and show that its value is close to the radius within which the enclosed overdensity is 200 times the mean matter density of the Universe, while theoretical models predict a larger value consistent with a low accretion rate. This points to a potential bias of optically selected clusters preferentially characterized by a high density at small scales compared to a pure mass-selected cluster sample., Comment: Accepted for publication in A&A. Added a discussion about prior parameters, as suggested by the reviewer. arXiv admin note: text overlap with arXiv:2103.05653

According to a particular interpretation of quantum mechanics, the causal role of human consciousness in the measuring process is called upon to solve a foundational problem called the "measurement problem". Traditionally, this interpretation is tied up with the metaphysics of substance dualism. As such, this interpretation of quantum mechanics inherits the dualist's mind-body problem. Our working hypothesis is that a process-based approach to the consciousness causes collapse interpretation (CCCI) -- leaning on Whitehead's solution to the mind-body problem -- offers a better metaphysical understanding of consciousness and its role in interpreting quantum mechanics. This article is the kickoff for such a research program in the metaphysics of science., Comment: Forthcoming in Manuscrito vol. 47, n.1, 2024. Special issue: "Scientific Process Ontology and Metaphysics". Url: https://www.scielo.br/j/man/i/2024.v47n1

We study interaction-mediated magnetism on the surface of ABC-multilayer graphene driven by its zero-energy topological flat bands. Using the random-phase approximation we treat onsite Hubbard repulsion and find multiple competing magnetic states, due to both intra- and inter-valley scattering, with the latter causing an enlarged magnetic unit cell. At half-filling and when the Hubbard repulsion is weak, we observe two different ferromagnetic orders. Once the Hubbard repulsion becomes more realistic, new ferrimagnetic orders arise with distinct incommensurate intra- or inter-valley scattering vectors depending on interaction strength and doping, leading to a multitude of competing magnetic states., Comment: Main text: 7 pages and 4 figures, Supplementary Material: 11 pages and 8 figures

Quantum turbulence indicators in dipolar Bose-Einstein condensed fluids, following emissions of vortex-antivortex pairs generated by a circularly moving detuned laser, are being provided by numerical simulations of the corresponding quasi-two-dimensional Gross-Pitaevskii formalism with repulsive contact interactions combined with tunable dipole-dipole strength. The critical velocities of two variants of a circularly moving obstacle are determined and analyzed for vortex-antivortex nucleation in the form of regular and cluster emissions. The turbulent dynamical behavior is predicted to follow closely the initial emission of vortex-antivortex pairs, relying on the expected Kolmogorov's classical scaling law, which is verified by the spectral analysis of the incompressible part of the kinetic energy. Within our aim to provide further support in the up-to-now investigations of quantum turbulence, which have been focused on non-dipolar Bose-Einstein condensates, we emphasize the role of dipole-dipole interactions in the fluid dynamics.

We present a new galaxy cluster search in the COSMOS field through the use of the Adaptive Matched Identifier of Clustered Objects (AMICO). We produced a new cluster and group catalogue up to $z=2$, by performing an innovative application of AMICO with respect to previous successful applications to wide-field surveys, in terms of depth (down to $r < 26.7$), small area covered ($1.69 deg^2$ of unmasked area) and redshift extent. This sample, and the comparative analysis we performed with the X-rays, allowed for the calibration of mass-proxy scaling relations up to $z=2$ and down to less than $10^{13} M_{sun}$ and constitutes the base for the refinement of the cluster model for future applications of AMICO, like the analysis of upcoming Euclid data. AMICO is based on an optimal linear matched filter and detects clusters in photometric galaxy catalogues using galaxy location, photometric redshift and, in the simplest case, one galaxy property. We used one magnitude as galaxy property, avoiding explicit use of galaxy colour, and performed 3 independent runs in the r, Y and H bands using both COSMOS2020 and COSMOS2015 galaxy catalogues. The final catalogue resulting from matching the results of the three runs contains 1269 and 666 candidate clusters with $S/N >3.0$ and $>3.5$, respectively. Most of the unmatched ones have $S/N <3.5$ which can be chosen as cut for a more robust sample. We assigned X-ray properties to our detections via matching with a public X-ray group sample and by estimating, for unmatched detections, X-ray properties at the location of AMICO candidates based on Chandra+XMM-Newton data. 622 are the candidates with X-ray flux estimate. This large sample allowed for the calibration of the scaling relations between AMICO mass-proxies and X-ray mass and the study of their redshift dependence for the selection of the most stable photometric bands., Comment: 16 pages, 15 figures, 3 tables, submitted to Astronomy & Astrophysics (A&A)

Successful data-driven science requires complex data engineering pipelines to clean, transform, and alter data in preparation for machine learning, and robust results can only be achieved when each step in the pipeline can be justified, and its effect on the data explained. In this framework, our aim is to provide data scientists with facilities to gain an in-depth understanding of how each step in the pipeline affects the data, from the raw input to training sets ready to be used for learning. Starting from an extensible set of data preparation operators commonly used within a data science setting, in this work we present a provenance management infrastructure for generating, storing, and querying very granular accounts of data transformations, at the level of individual elements within datasets whenever possible. Then, from the formal definition of a core set of data science preprocessing operators, we derive a provenance semantics embodied by a collection of templates expressed in PROV, a standard model for data provenance. Using those templates as a reference, our provenance generation algorithm generalises to any operator with observable input/output pairs. We provide a prototype implementation of an application-level provenance capture library to produce, in a semi-automatic way, complete provenance documents that account for the entire pipeline. We report on the ability of our implementations to capture provenance in real ML benchmark pipelines and over TCP-DI synthetic data. We finally show how the collected provenance can be used to answer a suite of provenance benchmark queries that underpin some common pipeline inspection questions, as expressed on the Data Science Stack Exchange., Comment: 37 pages, 27 figures, submitted to a journal

By studying the dynamic stability of Bose-Einstein condensed binary mixtures trapped on the surface of an ideal two-dimensional spherical bubble, we show how the Rabi coupling between the species can modulate the interactions leading to parametric resonances. In this spherical geometry, the discrete unstable angular modes drive both phase separations and spatial patterns, with Faraday waves emerging and coexisting with an immiscible phase. Noticeable is the fact that, in the context of discrete kinetic energy spectrum, the only parameters to drive the emergence of Faraday waves are the $s-wave$ contact interactions and the Rabi coupling. Once analytical solutions for population dynamics are obtained, the stability of homogeneous miscible species is investigated through Bogoliubov-de Gennes and Floquet methods, with predictions being analysed by full numerical solutions applied to the corresponding time-dependent coupled formalism., Comment: 17 pages, 15 figures

In this study, we investigate a recent finding based on strong lensing observations, which suggests that the sub-halos observed in clusters exhibit greater compactness compared to those predicted by $\Lambda$CDM simulations. To address this discrepancy, we performed a comparative analysis by comparing the cumulative mass function of sub-halos and the $M_{\text{sub}}$-$V_{\text{circ}}$ relation between observed clusters and 324 simulated clusters from The Three Hundred project, focusing on re-simulations using GADGET-X and GIZMO-SIMBA baryonic models. The sub-halos' cumulative mass function of the GIZMO-SIMBA simulated clusters agrees with observations, while the GADGET-X simulations exhibit discrepancies in the lower sub-halo mass range possibly due to its strong SuperNova feedback. Both GADGET-X and GIZMO-SIMBA simulations demonstrate a redshift evolution of the sub-halo mass function and the $V_{max}$ function, with slightly fewer sub-halos observed at lower redshifts. Neither the GADGET-X nor GIZMO-SIMBA(albeit a little closer) simulated clusters' predictions for the $M_{\text{sub}}$-$V_{\text{circ}}$ relation align with the observational result. Further investigations on the correlation between sub-halo/halo properties and the discrepancy in the $M_{\text{sub}}$-$V_{\text{circ}}$ relation reveals that the sub-halo's half mass radius and galaxy stellar age, the baryon fraction and sub-halo distance from the cluster's centre, as well as the halo relaxation state play important roles on this relation. Nevertheless, we think it is still challenging in accurately reproducing the observed $M_{\text{sub}}$-$V_{\text{circ}}$ relation in our current hydrodynamic cluster simulation under the standard $\Lambda$CDM cosmology., Comment: see the companion paper, Meneghetti et al. (2023), on today's arxiv list

Previous studies have revealed that the estimated probability of galaxy-galaxy strong lensing in observed galaxy clusters exceeds the expectations from the $\Lambda$ Cold Dark Matter cosmological model by one order of magnitude. We aim to understand the origin of this excess by analyzing a larger set of simulated galaxy clusters and investigating how the theoretical expectations vary under different adopted prescriptions and numerical implementations of star formation and feedback in simulations. We perform a ray-tracing analysis of 324 galaxy clusters from the Three Hundred project, comparing the Gadget-X and Gizmo-Simba runs. These simulations, which start from the same initial conditions, are performed with different implementations of hydrodynamics and galaxy formation models tailored to match different observational properties of the Intra-Cluster-Medium and cluster galaxies. We find that galaxies in the Gizmo-Simba simulations develop denser stellar cores than their Gadget-X counterparts. Consequently, their probability for galaxy-galaxy strong lensing is higher by a factor of $\sim 3$. This increment is still insufficient to fill the gap with observations, as a discrepancy by a factor $\sim 4$ still persists. In addition, we find that several simulated galaxies have Einstein radii that are too large compared to observations. We conclude that a persistent excess of galaxy-galaxy strong lensing exists in observed galaxy clusters. The origin of this discrepancy with theoretical predictions is still unexplained in the framework of the cosmological hydrodynamical simulations. This might signal a hitherto unknown issue with either the simulation methods or our assumptions regarding the standard cosmological model., Comment: 6 pages, 2 figures, accepted for publication on A&A Letters. See companion paper Srivastava et al. (2023)

Magic-angle twisted bilayer graphene (MATBG) has been extensively explored both theoretically and experimentally as a suitable platform for a rich and tunable phase diagram that includes ferromagnetism, charge order, broken symmetries, and unconventional superconductivity. In this work, we investigate the intricate interplay between long-range electron-electron interactions, spin fluctuations, and superconductivity in MATBG. By employing a low-energy model for MATBG that captures the correct shape of the flat bands, we explore the effects of short- and long-range interactions on spin fluctuations and their impact on the superconducting (SC) pairing vertex in the Random Phase Approximation (RPA). We find that the SC state is notably influenced by the strength of long-range Coulomb interactions. Interestingly, our RPA calculations indicate that there is a regime where the system can traverse from a magnetic phase to the SC phase by \emph{increasing} the relative strength of long-range interactions compared to the on-site ones. These findings underscore the relevance of electron-electron interactions in shaping the intriguing properties of MATBG and offer a pathway for designing and controlling its SC phase., Comment: Revised and expanded version. 11 pages, 7 figures

We constrain the $\Lambda$CDM cosmological parameter $\sigma_{8}$ by applying the extreme value statistics for galaxy cluster mass on the AMICO KiDS-DR3 catalog. We sample the posterior distribution of the parameters by considering the likelihood of observing the largest cluster mass value in a sample of $N_{\textrm{obs}} = 3644$ clusters with intrinsic richness $\lambda^{*} > 20$ in the redshift range $z\in[0.10, 0.60]$. We obtain $\sigma_{8}=0.90_{-0.18}^{+0.20}$, consistent within $1\sigma$ with the measurements obtained by the Planck collaboration and with previous results from cluster cosmology exploiting AMICO KiDS-DR3. The constraints could improve by applying this method to forthcoming missions, such as $\textit{Euclid}$ and LSST, which are expected to deliver thousands of distant and massive clusters., Comment: 11 pages, 9 figures, 4 tables

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems.

Although a superconductor has no DC losses, a superconducting system does have significant losses, especially when it comes to power supply. Here, we study two different power supply systems. The first, a conventional one, consists of a transformer and a diode bridge operating at room temperature, plus current leads that allow the current to flow from the room-temperature medium to the cryogenic medium. The second consists of a transformer with a superconducting secondary winding, combined with a diode bridge operating at cryogenic temperature, thus dispensing with the need for current leads. We are experimentally comparing the performance of conventional and superconducting transformers, as well as the performance of a diode bridge at ambient and cryogenic temperatures. Our results indicate that the prototype superconducting transformer developed has lower winding resistance and secondary leakage inductance than the conventional transformer. In addition, we found that only certain diodes are suitable for operation at cryogenic temperatures. Finally, the diode bridge made from adapted diodes shows reduced losses at cryogenic temperature. This experimental work is the first step towards the realization of a complete power supply system for a superconducting device., Comment: in French language, Symposium de Genie Electrique (SGE 2023), Jul 2023, Lille, France

The effect of two- and three-body interactions on the modulation instability (MI) domain formation of a spin-orbit (SO) and Rabi-coupled Bose-Einstein condensate is studied within a quasi-one-dimensional model. To this aim, we perform numerical and analytical investigations of the associated dispersion relations derived from the corresponding coupled Gross-Pitaevskii equation. The interplay between the linear (SO and Rabi) couplings with the nonlinear cubic-quintic interactions are explored in the mixture, considering miscible and immiscible configurations, with a focus on the impact in the analysis of experimental realizations with general binary coupled systems, in which nonlinear interactions can be widely varied together with linear couplings., Comment: 11 pages, 10 figures

In the anti-Stokes excitation of trivalent rare-earth ions (RE3+), the excitation photons energy is smaller than that of a given absorption transition, and the energy mismatch can be compensated by phonons annihilation from the host lattice. Since the phonon occupation number increases with temperature, heating the system generally increases the efficiency of anti-Stokes excitation. Here, we exploited the intrinsic heating associated with light-to-heat conversion in the interaction of excitation laser light with metallic nanoparticles (Ag or Au) on the surface of submicrometric particles of NdxY1.00-xAl3(BO3)4 (x = 0.10, 0.20, and 1.00) in order to enhance the efficiency of the anti-Stokes excitation at 1064 nm. Several upconversion emissions are observed from 600 nm to 880 nm, the most intense being at 750 nm due to the Nd3+ transition {4F7/2, 4S3/2} - 4I9/2. Giant enhancements are demonstrated, when compared to undecorated NdxY1.00-xAl3(BO3)4 particles. The present results can be expanded to other luminescent materials as well as excitation wavelengths.

Synthetic dimensions (SDs) opened the door for exploring previously inaccessible phenomena in high-dimensional synthetic space. However, construction of synthetic lattices with desired coupling properties is a challenging and unintuitive task, largely limiting the exploration and current application of SD dynamics. Here, we overcome this challenge by using deep learning artificial neural networks (ANNs) to validly design the dynamics in SDs. We use ANNs to construct a lattice in real space that has a predesigned spectrum of mode eigenvalues. By employing judiciously chosen perturbations (wiggling of waveguides), we show experimentally and theoretically resonant mode coupling and tailored dynamics in SDs, which leads to effective transport or confinement of a complex beam profile. As an enlightening example, we demonstrate morphing of light into a topologically protected edge mode in ANN-designed Su-Schrieffer-Heeger photonic lattices. Such ANN-assisted construction of SDs advances towards utopian networks, opening new avenues in fundamental research beyond geometric limitations. Our findings may offer a flexible and efficient solution for mode lasing, optical switching, and communication technologies., Comment: 14 pages and 4 figures

In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This prompted us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the approach to estimate age-specific sources of HIV infection in Amsterdam MSM transmission networks between 2010-2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models.

A kinetic system has an absolute concentration robustness (ACR) for a molecular species if its concentration remains the same in every positive steady state of the system. Just recently, a condition that sufficiently guarantees the existence of an ACR in a rank-one mass-action kinetic system was found. In this paper, it will be shown that this ACR criterion does not extend in general to power-law kinetic systems. Moreover, we also discussed in this paper a necessary condition for ACR in multistationary rank-one kinetic system which can be used in ACR analysis. Finally, a concept of equilibria variation for kinetic systems which are based on the number of the system's ACR species will be introduced here.

We prove that in any rank one symmetric space of non-compact type $M\in\{\mathbb{R} H^n,\mathbb{C} H^m,\mathbb{H} H^m,\mathbb{O} H^2\}$, geodesic spheres are uniformly quantitatively stable with respect to small $C^1$-volume preserving perturbations. We quantify the gain of perimeter in terms of the $W^{1,2}$-norm of the perturbation, taking advantage of the explicit spectral gap of the Laplacian on geodesic spheres in $M$. As a consequence, we give a quantitative proof that for small volumes, geodesic spheres are isoperimetric regions among all sets of finite perimeter., Comment: 24 pages

A principled understanding of generalization in deep learning may require unifying disparate observations under a single conceptual framework. Previous work has studied \emph{grokking}, a training dynamic in which a sustained period of near-perfect training performance and near-chance test performance is eventually followed by generalization, as well as the superficially similar \emph{double descent}. These topics have so far been studied in isolation. We hypothesize that grokking and double descent can be understood as instances of the same learning dynamics within a framework of pattern learning speeds. We propose that this framework also applies when varying model capacity instead of optimization steps, and provide the first demonstration of model-wise grokking., Comment: ML Safety Workshop, 36th Conference on Neural Information Processing Systems (NeurIPS 2022)

The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, which require the artificial perception of the railway environment through different types of sensors, including cameras, LiDARs, wheel encoders, and inertial measurement units. A promising approach for processing such sensory data is the use of deep learning models, which proved to achieve excellent performance in other application domains, as robotics and self-driving cars. However, testing new algorithms and solutions requires the availability of a large amount of labeled data, acquired in different scenarios and operating conditions, which are difficult to obtain in a real railway setting due to strict regulations and practical constraints in accessing the trackside infrastructure and equipping a train with the required sensors. To address such difficulties, this paper presents a visual simulation framework able to generate realistic railway scenarios in a virtual environment and automatically produce inertial data and labeled datasets from emulated LiDARs and cameras useful for training deep neural networks or testing innovative algorithms. A set of experimental results are reported to show the effectiveness of the proposed approach., Comment: Under review

Research in Fairness, Accountability, Transparency, and Ethics (FATE) has established many sources and forms of algorithmic harm, in domains as diverse as health care, finance, policing, and recommendations. Much work remains to be done to mitigate the serious harms of these systems, particularly those disproportionately affecting marginalized communities. Despite these ongoing harms, new systems are being developed and deployed which threaten the perpetuation of the same harms and the creation of novel ones. In response, the FATE community has emphasized the importance of anticipating harms. Our work focuses on the anticipation of harms from increasingly agentic systems. Rather than providing a definition of agency as a binary property, we identify 4 key characteristics which, particularly in combination, tend to increase the agency of a given algorithmic system: underspecification, directness of impact, goal-directedness, and long-term planning. We also discuss important harms which arise from increasing agency -- notably, these include systemic and/or long-range impacts, often on marginalized stakeholders. We emphasize that recognizing agency of algorithmic systems does not absolve or shift the human responsibility for algorithmic harms. Rather, we use the term agency to highlight the increasingly evident fact that ML systems are not fully under human control. Our work explores increasingly agentic algorithmic systems in three parts. First, we explain the notion of an increase in agency for algorithmic systems in the context of diverse perspectives on agency across disciplines. Second, we argue for the need to anticipate harms from increasingly agentic systems. Third, we discuss important harms from increasingly agentic systems and ways forward for addressing them. We conclude by reflecting on implications of our work for anticipating algorithmic harms from emerging systems., Comment: Accepted at FAccT 2023

We provide novel constraints on the parameters defining the universal pressure profile (UPP) within clusters of galaxies, and explore their dependence on the cluster mass and redshift, from measurements of Sunyaev-Zel'dovich Compton-$y$ profiles. We employ both the $\textit{Planck}$ 2015 MILCA and the ACT-DR4 $y$ maps over the common $\sim 2,100\,\text{deg}^2$ footprint. We combine existing cluster catalogs based on KiDS, SDSS and DESI observations, for a total of 23,820 clusters spanning the mass range $10^{14.0}\,\text{M}_{\odot}

We investigate the main tensions within the current standard model of cosmology from the perspective of the main statistics of cosmic voids, using the final BOSS DR12 data set. For this purpose, we present the first estimate of the $S_8\equiv \sigma_8\sqrt{\Omega_{\rm m}/0.3}$ and $H_0$ parameters obtained from void number counts and shape distortions. To analyze void counts we relied on an extension of the popular volume-conserving model for the void size function, tailored to the application on data, including geometric and dynamic distortions. We calibrated the two nuisance parameters of this model with the official BOSS collaboration mock catalogs and propagated their uncertainty through the statistical analysis of the BOSS void number counts. The constraints from void shapes come from the study of the geometric distortions of the stacked void-galaxy cross-correlation function. In this work we focus our analysis on the $\Omega_{\rm m}$-$\sigma_8$ and $\Omega_{\rm m}$-$H_0$ parameter planes and derive the marginalized constraints $S_8 = 0.813^{+0.093}_{-0.068}$ and $H_0 = 67.3^{+10.0}_{-9.1} \ \mathrm{km} \ \mathrm{s}^{-1} \ \mathrm{Mpc}^{-1}$, which are fully compatible with constraints from the literature. These results are expected to notably improve in precision when analyzed jointly with independent probes and will open a new viewing angle on the rising cosmological tensions in the near future., Comment: 8 pages, 5 figures, published in A&A

We present the first cosmological constraints derived from the analysis of the void size function. This work relies on the final BOSS DR12 data set, a large spectroscopic galaxy catalog, ideal for the identification of cosmic voids. We extract a sample of voids from the distribution of galaxies and we apply a cleaning procedure aimed at reaching high levels of purity and completeness. We model the void size function by means of an extension of the popular volume-conserving model, based on two additional nuisance parameters. Relying on mock catalogs specifically designed to reproduce the BOSS DR12 galaxy sample, we calibrate the extended size function model parameters and validate the methodology. We then apply a Bayesian analysis to constrain the $\Lambda$CDM model and one of its simplest extensions, featuring a constant dark energy equation of state parameter, $w$. Following a conservative approach, we put constraints on the total matter density parameter and the amplitude of density fluctuations, finding $\Omega_{\rm m}=0.29 \pm 0.06$ and $\sigma_8=0.79^{+0.09}_{-0.08}$. Testing the alternative scenario, we derive $w=-1.1\pm 0.2$, in agreement with the $\Lambda$CDM model. These results are independent and complementary to those derived from standard cosmological probes, opening up new ways to identify the origin of potential tensions in the current cosmological paradigm., Comment: 24 pages, 13 figures, 3 tables, accepted by ApJ

We use hydrodynamical cosmological simulations to test the differences between cold and self-interacting dark matter models (CDM and SIDM) in the mass range of massive galaxies ($10^{12}M_{\odot}h^{-1}10^{13}M_{\odot}h^{-1}$) show cored profiles, while the least massive ones ($M<10^{12.5}M_{\odot}h^{-1}$) have cuspier profiles. Finally, we compare the predictions of our simulations to observational results, by looking at the dark matter fractions and the distribution of strong lensing Einstein radii. We find that in SIDM the DM-fractions decrease more rapidly with increasing stellar mass than in CDM, leading to lower fractions at $M_{*}>10^{11}M_{\odot}$, a distinctive signature of SIDM. At the same time, the distribution of Einstein radii, derived from both CDM and SIDM hydro runs, is comparable to observed samples of strong lenses with $M>10^{13}M_{\odot}h^{-1}$. We conclude that the interplay between self-interaction and baryons can greatly reduce the expected differences between CDM and SIDM models at this mass scale, and that techniques able to separate the dark and luminous mass in the inner regions of galaxies are needed to constrain self-interactions., Comment: 13 pages, 12 figures

We investigate the role of the Nd3+ content on the PA characteristics in NdxY1.00-xAl3(BO3)4 particles with x ranging from 0.05 to 1.00. It is known that the replacement of Y3 by Nd3+ into the YAl3(BO3)4 crystalline structure can introduce strong modifications of the lattice properties as well as in the photoluminescence characteristics, such as luminescence concentration quenching and broadening of spectral lines. Despite that, we observe, for low x (<= 0.20) an energy-looping preceding the PA-like that ensues for x >= 0.40. It is associated to the proximity among the Nd3+ ions, fundamental to the electric dipole-electric dipole interaction responsible for the Nd3+ energy transfer [4F3/2, 4I9/2] - [4I15/2, 4I15/2]. We discuss the present results focusing on emerging technologies with development of ultra-sensitive thermal sensors, and super-resolution imaging thanks to the giant nonlinearities in the input-output power dependences.

Upcoming cosmological surveys will provide unprecedented amount of data, which will require innovative statistical methods to maximize the scientific exploitation. Standard cosmological analyses based on abundances, two-point and higher-order statistics of cosmic tracers have been widely used to investigate the properties of the cosmic web and Large Scale Structure. However, these statistics can only exploit a subset of the entire information content available. Our goal is thus to implement new data analysis techniques based on machine learning to extract cosmological information through forward modelling, by directly exploiting the spatial coordinates and other observed properties of galaxies and galaxy clusters. Specifically, we investigated a new representation of large-scale structure data in the form of graphs. This data format can be directly fed to Graph Neural Networks, a recently proposed class of supervised Deep Learning algorithms. We tested the method on dark matter halo catalogues in different cosmologies, finding promising results. In particular, the method can discriminate among different dark energy models with high accuracy, through both binary classification ($99\%-$accuracy) and multi-class classification ($97\%-$accuracy). Moreover, it provides constraints on the value of $w_0$, through regression, with high precision., Comment: Submitted for publication in the ML4ASTRO conference proceeding book

Galaxy clusters and cosmic voids, the most extreme objects of our Universe in terms of mass and size, trace two opposite sides of the large-scale matter density field. By studying their abundance as a function of their mass and radius, respectively, i.e. the halo mass function (HMF) and void size function (VSF), it is possible to achieve fundamental constraints on the cosmological model. While the HMF has already been extensively exploited providing robust constraints on the main cosmological model parameters (e.g. $\Omega_{\rm m}$, $\sigma_8$ and $S_8$), the VSF is still emerging as a viable and effective cosmological probe. Given the expected complementarity of these statistics, in this work we aim at estimating the costraining power deriving from their combination. To this end, we exploit realistic mock samples of galaxy clusters and voids extracted from state-of-the-art large hydrodynamical simulations, in the redshift range $0.2 \leq z \leq 1$. We perform an accurate calibration of the free parameters of the HMF and VSF models, needed to take into account the differences between the types of mass tracers used in this work and those considered in previous literature analyses. Then, we obtain constraints on $\Omega_{\rm m}$ and $\sigma_8$ by performing a Bayesian analysis. We find that cluster and void counts represent powerful independent and complementary probes to test the cosmological framework. In particular, the constraining power of the HMF on $\Omega_{\rm m}$ and $\sigma_8$ improves drastically with the VSF contribution, increasing the $S_8$ constraint precision by a factor of about 60%., Comment: 14 pages, 8 figures - published in MNRAS

Herein, we invesgate the consequence of local voltage pulses on the adsorption state of single indigo molecules on the Cu(111) surface as well as on the atomic structure underneath the molecule. With a scanning tunneling microscope, at 5 K, intact molecules are imaged as two lobes corresponding to the electron density of each indoxyl moiety of the molecule which are connected by a carbon double bond. Then, two short successive voltage pulses with the tip placed above the molecule generate irreversible modifications, as revealed by consecutive scanning tunneling microscopy (STM) imaging. Density-functional theory calculations coupled to STM image calculations indicate the creation of a double surface vacancy of copper surface atoms below the oxygen atom of the indigo molecule as the most plausible scenario. These extracted copper atoms are stabilized as adatoms by the indigo oxygens, oxidizing each copper adatom to 0.32 electron.

We prove that geodesic balls centered at some base point are isoperimetric in the real hyperbolic space $H_{\mathbb R}^n$ endowed with a smooth, radial, strictly log-convex density on the volume and perimeter. This is an analogue of the result by G. R. Chambers for log-convex densities on $\mathbb R^n$. As an application we prove that in any rank one symmetric space of non-compact type, geodesic balls are isoperimetric in a class of sets enjoying a suitable notion of radial symmetry., Comment: 17 pages, 5 figures. Added references. Generalized Definition 1.2 to the octonionic case, and simplified the argument in Section 4

We prove local-in-time existence of smooth solutions to the semigeostrophic equations in the general setting of smooth, bounded and simply connected domains of $\mathbb{R}^2$ endowed with an arbitrary conformally flat metric and non-vanishing Coriolis term. We present a construction taking place in Eulerian coordinates, avoiding the classical reformulation in dual variables, used in the flat case with constant Coriolis force, but lacking in this general framework., Comment: 18 pages

Chang'e-3(CE-3) has been the first spacecraft to soft-land on the Moon since the Soviet Union's Luna 24 in 1976. The spacecraft arrived at Mare Imbrium on December 14, 2013 and the same day, Yutu lunar rover separated from lander to start its exploration of the surface and the subsurface around the landing site. The rover was equipped, among other instruments, with two Lunar Penetrating Radar systems (LPR) having a working frequency of 60 and 500 MHz. The radars acquired data for about two weeks while the rover was slowly moving along a path of about 114 m. At Navigation point N0209 the rover got stacked into the lunar soil and after that only data at fixed position could be collected. The low frequency radar data have been analyzed by different authors and published in two different papers, which reported totally controversial interpretations of the radar cross sections. The present study is devoted to resolve such controversy carefully analyzing and comparing the data collected on the Moon by Yutu rover and on Earth by a prototype of LRP mounted onboard a model of the CE-3 lunar rover. Such analysis demonstrates that the deep radar features previously ascribed to the lunar shallow stratigraphy are not real reflectors, rather they are signal artefacts probably generated by the system and its electromagnetic interaction with the metallic rover.

The dynamical vortex production, with a trap-confining time-dependent stirred potential, is studied by using mass-imbalanced cold-atom coupled Bose-Einstein condensates (BEC). The vortex formation is explored by considering that both coupled species are confined by a pancake-like harmonic trap, slightly modified elliptically by a time-dependent periodic potential, with the characteristic frequency enough larger than the transversal trap frequency. The approach is applied to the experimentally accessible binary mixtures $^{85}$Rb-$^{133}$Cs and $^{85}$Rb-$^{87}$Rb, which allow us to verify the effect of mass differences in the dynamics. For both species, the time evolutions of the respective energy contributions, together with associated velocities, are studied in order to distinguish turbulent from non-turbulent flows. By using the angular momentum and moment of inertia mean values, effective classical rotation frequencies are suggested, which are further considered within simulations in the rotating frame without the stirring potential. Spectral analysis is also provided for both species, with the main focus being the incompressible kinetic energies. In the transient turbulent regime, before stable vortex patterns are produced, the characteristic $k^{-5/3}$ Kolmogorov behavior is clearly identified for both species at intermediate momenta $k$ above the inverse Thomas-Fermi radial positions, further modified by the universal $k^{-3}$ scaling at momenta higher than the inverse of the respective healing lengths. Emerging from the mass-imbalanced comparison, relevant is to observe that, as larger is the mass difference, much faster is the dynamical production of stable vortices., Comment: 17 pages, 16 figures

In this paper, we assess the impact of numerical resolution and of the implementation of energy input from AGN feedback models on the inner structure of cluster sub-haloes in hydrodynamic simulations. We compare several zoom-in re-simulations of a sub-sample of the cluster-sized haloes studied in Meneghetti et al. (2020), obtained by varying mass resolution, softening length and AGN energy feedback scheme. We study the impact of these different setups on the subhalo abundances, their radial distribution, their density and mass profiles and the relation between the maximum circular velocity, which is a proxy for subhalo compactness. Regardless of the adopted numerical resolution and feedback model, subhaloes with masses Msub < 1e11Msun/h, the most relevant mass-range for galaxy-galaxy strong lensing, have maximum circular velocities ~30% smaller than those measured from strong lensing observations of Bergamini et al. (2019). We also find that simulations with less effective AGN energy feedback produce massive subhaloes (Msub> 1e11 Msun/h ) with higher maximum circular velocity and that their Vmax - Msub relation approaches the observed one. However the stellar-mass number count of these objects exceeds the one found in observations and we find that the compactness of these simulated subhaloes is the result of an extremely over-efficient star formation in their cores, also leading to larger-than-observed subhalo stellar mass. We conclude that simulations are unable to simultaneously reproduce the observed stellar masses and compactness (or maximum circular velocities) of cluster galaxies. Thus, the discrepancy between theory and observations that emerged from the analysis of Meneghetti et al. (2020) persists. It remains an open question as to whether such a discrepancy reflects limitations of the current implementation of galaxy formation models or the LCDM paradigm., Comment: 11 pages, 10 figures, abstract is redacted to fit arXiv character count limit

Meneghetti et al. (2020) recently reported an excess of galaxy-galaxy strong lensing (GGSL) in galaxy clusters compared to expectations from the LCDM cosmological model. Theoretical estimates of the GGSL probability are based on the analysis of numerical hydrodynamical simulations in the LCDM cosmology. We quantify the impact of the numerical resolution and AGN feedback scheme adopted in cosmological simulations on the predicted GGSL probability and determine if varying these simulation properties can alleviate the gap with observations. We repeat the analysis of Meneghetti et al. (2020) on cluster-size halos simulated with different mass and force resolutions and implementing several independent AGN feedback schemes. We find that improving the mass resolution by a factor of ten and twenty-five, while using the same galaxy formation model that includes AGN feedback, does not affect the GGSL probability. We find similar results regarding the choice of gravitational softening. On the contrary, adopting an AGN feedback scheme that is less efficient at suppressing gas cooling and star formation leads to an increase in the GGSL probability by a factor between three and six. However, we notice that such simulations form overly massive subhalos whose contribution to the lensing cross-section would be significant while their Einstein radii are too large to be consistent with the observations. The primary contributors to the observed GGSL cross-sections are subhalos with smaller masses, that are compact enough to become critical for lensing. The population with these required characteristics appears to be absent in simulations., Comment: 13 pages, 11 figures. Submitted for publication on Astronomy and Astrophysics

Purpose: In this study we propose to identify significant economic, political, and social factors that helped contribute to the incidence and mortality of SARS-CoV-2 around the world during the first seven months of the COVID-19 pandemic. Methods: Retrospective analyses were conducted using data acquired by the World Health Organization regarding the number of new cases and deaths from 76 countries from January through July 2020. Hierarchical cluster and principal component analyses were used to categorize different countries based on their ability to control the outbreak based on number of cases and deaths throughout these initial months of the pandemic. Furthermore, using non-parametrical analyses, different variables were correlated with multiple economic, political, and social indexes. Results: Multiple significant correlations were found, including the control of corruption, government effectiveness, and the human life indicator, with indexes containing the higher correlations demonstrating the capability of individual countries to help drive the course of this pandemic. Geographical hotspots were identified that required further attention to aid in outbreak control. Conclusion: Collectively, the findings in this study indicate that multiple factors beyond the biological should be considered in the implementation of measures to contain this pandemic, Comment: 18 pages, 5 figures