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We present Cepheid-based distances to two canonical AGN: NGC 4303 (M 61) and NGC 1068. Data were obtained using the Hubble Space Telescope with nonredundant time spacing over 12 visits for each target, and observations were made with the F555W and F814W filters. We found 32,694 point sources in NGC 4303, and 130 of these were determined to be strong Cepheid candidates with periods ranging ~$13-93$ days. In NGC 1068, we found 20,207 point sources, where 51 of these were strong Cepheid candidates with periods ~$14-92$ days. We fit the period$-$luminosity relationship, calibrated based on a geometric distance to the LMC by Riess et al. (2019), to our Cepheid candidates in each galaxy and correct for potential effects of metallicity. Using a distance constraint for the LMC given by Pietrzyński et al. (2019), this yields a distance modulus of $μ= 31.083 \pm 0.035$ mag for NGC 4303 and $μ= 30.150 \pm 0.106$ mag for NGC 1068. Thus, we measure distances of $D = 16.47 \pm 0.27$ Mpc to NGC 4303 and $D = 10.72 \pm 0.52$ Mpc to NGC 1068.

We construct new families of type-IIB supergravity solutions by employing TsT transformations on the ten-dimensional geometry that arises after the uplift of the five-dimensional soliton solution of Anabalón, Nastase, and Oyarzo. In particular, we identify two marginal and two dipole deformations of the uplifted geometry. We then analyse a plethora of holographic observables -- including Wilson loops, `t~Hooft loops, Page charges, entanglement entropy, and central charge -- and compare their behaviour across the different deformed backgrounds.

Future $e^+e^-$ colliders at the Z pole place strong demands of $\frac{δL}{L}<10^{-4}$ on the integrated luminosity measurement. Small angle Bhabha scattering (SABS) remains the standard channel, while diphoton ($γγ$) events provide a complementary measurement. This contribution summarizes recent work on two dominant uncertainties. First, we investigate backgrounds to the diphoton channel and find that SABS and low-invariant-mass neutral hadrons are the most significant backgrounds. A gradient boosted decision tree (BDTG) is used to classify events by particle ID. The classification results show the existing and upgraded forward tracker and luminosity calorimeter (LumiCal) designs reject neutral hadrons but only the LumiCal upgrade can reject SABS at $\frac{δL}{L}<10^{-4}$. Second, we solve the beam deflection bias problem on an event-by-event basis using two machine learning algorithms. A BDTG and the newly written Adaptive Symbolic Memetic Regression (ASMR) are trained on beam deflection data. ASMR outperforms BDTG and provides a reduced uncertainty of $5\times10^{-6}$ for beam deflection.

We present the results of a multi-cycle Chandra program to systematically monitor the X-ray variability of 10 weak-line quasars (WLQs) that previously had limited multi-epoch X-ray observations. Three new Chandra 2.8 to 8.2 ks observations were obtained for each WLQ with C$\,$IV rest-frame equivalent widths (REWs) $\lesssim 10$ Å, substantially improving the monitoring data quality of WLQs and our ability to characterize their long-term X-ray variability behavior. We observe recurrent extreme X-ray variability in the historically variable WLQ SDSS J1539+3954, with an X-ray flux rise of a factor of $\gtrsim 6$ between 2023 and 2024 ($\gtrsim 21$ relative to 2013). Another previously X-ray weak WLQ in the sample, SDSS J0825+1155, underwent a significant X-ray flux variation by a factor of $\gtrsim 14$ between 2019 and 2023. We find the fraction of WLQs exhibiting evidence of extreme X-ray variability to be $0.20^{+0.17}_{-0.07}$. In the context of the thick disk and outflow (TDO) model, the substantial fraction of WLQs displaying extreme X-ray variability may suggest that the variability is driven by the intrinsic motion of the TDO wind rather than changes in the height of the TDO di

There are many different theoretical explanations for the formation of high-density Mercury-like planets, but concrete evidence for any of these formation mechanisms remains elusive. A popular explanation for dense planets is the collisional hypothesis, which states that iron-rich planets can be formed as the products of high-energy, mantle-stripping impacts. Planetesimal collision simulations predict that higher-velocity collisions can form higher-density planets. Motivated by the characteristics of the high-density, short-period (P=0.3d) GJ 367b, we study the results of previously-published smoothed-particle hydrodynamics (SPH) simulations on exoplanet collisions, combining these with models describing the likely collision velocities of these objects, to investigate the relationship between the core mass fractions (CMFs) of exoplanets, their masses, and their orbital periods. We predict that collisionally-produced super-Mercuries should be more common (and more dense) at low masses and short orbital periods. This correlation may enable us to pinpoint the formation mechanism of super-Mercuries as the population of observed targets grows. Afterwards, we connect our hypothesis to th

Algorithmic lending has transformed the consumer credit landscape, with machine learning models commonly facilitating underwriting decisions. To comply with fair lending laws, these algorithms exclude legally protected characteristics, such as race and gender. Yet algorithmic underwriting can still inadvertently favor certain groups, prompting concerns about whether lending algorithms exhibit discriminatory behavior. Using proprietary loan-level data from a major U.S. fintech platform, we audit lending decisions across approximately 80,000 personal loans. We find that loans made to men and Black borrowers yielded lower profits than loans to other groups, suggesting that men and Black borrowers benefited from relatively favorable pricing. We trace these disparities to miscalibration in the platform's underwriting model, which overestimates risk for women and underestimates risk for Black borrowers. We then show that one could correct this miscalibration -- and the corresponding disparities -- by including race and gender in underwriting models, illustrating a tension between competing notions of fairness.

Gravitational waves (GWs) serve as standard sirens by directly encoding the luminosity distance to their source. When the host galaxy redshift is known, for example, through observation of an electromagnetic (EM) counterpart, GW detections can provide an independent measurement of the Hubble constant, $H_0$. However, even in the absence of an EM counterpart, inferring $H_0$ is possible through the dark siren method. In this approach, every galaxy in the GW localization volume is considered a potential host that contributes to a measurement of $H_0$, with redshift information supplied by galaxy catalogs. Using mock galaxy catalogs, we explore the effect of catalog incompleteness on dark siren measurements of $H_0$. We find that in the case of well-localized GW events, if GW hosts are found in all galaxies with host halo masses $M_h > 2 \times10^{11} M_{\odot}h^{-1}$, catalogs only need to be complete down to the 1% brightest magnitude $M_i < -22.43$ to draw an unbiased, informative posterior on H0. We demonstrate that this is a direct result of the clustering of fainter galaxies around brighter and more massive galaxies. For a mock galaxy catalog without clustering, or for GW

Future electron-positron ($\ee$) colliders, operating as Higgs factories or Z factories, promise unprecedented precision electroweak measurements that are vital to testing the Standard Model (SM) and exploring physics beyond it. Here we present work on the precision of integrated luminosity ($\mathcal{L}$) and center-of-mass energy ($\sqrt{s}$), measurements that are needed to make future precision measurements possible. We also conduct these studies for the International Linear Collider (ILC) from the Z pole ($m_{Z}$) to 1~TeV to provide a comprehensive study of these issues for future $\ee$ colliders. Paths to 100 parts-per-million (ppm) precision on $\mathcal{L}$ are presented, with focus on small-angle Bhabha scattering (SABS) and two-photon production (diphotons, $γγ$). Previous studies found that beam deflection of SABS events introduce biases on $\mathcal{L}$ of $10^{-2}$. To address this, we present a novel method that uses Møller scattering with SABS to measure beam deflection and minimize its effect on $\mathcal{L}$. We present a proposal for a Highly Granular Luminosity Calorimeter, the GLIP LumiCal, and its design considerations. We demonstrate that the GLIP LumiCal can

Future warfare will occur in more complex, fast-paced, ill-structured, and demanding conditions that will stress current Command and Control (C2) systems. Without modernization, these C2 systems may fail to maintain overmatch against adversaries. We previously proposed robust partnerships between humans and artificial intelligence systems, and directly focusing on C2, we introduced how intelligent technologies could provide future overmatch through streamlining the C2 operations process, maintaining unity of effort across formations, and developing collective knowledge systems that adapt to battlefield dynamics across missions. Future C2 systems must seamlessly integrate human and machine intelligence to achieve decision advantage over adversaries while overcoming "new" challenges due to the technological advances driving fundamental changes in effective teaming, unity of effort, and meaningful human control. Here, we describe "new" C2 challenges and discuss pathways to transcend them, such as AI-enabled systems with effective human machine interfaces.

We present work on quantifying the minimum requirements for beam polarization precision at future $e^+e^-$ Higgs factories. We find that, under the assumption of a high electron beam polarization ($P_-$) that the positron polarization ($P_+$) is of key importance but for reasons both known and newly discovered. We have discovered that improved positron polarization leads to a less strict requirement on the beam polarization precision for measurements that scale only with the effective polarization, $P_\mathrm{eff}$. Conversely, measurements that scale with the product of beam polarizations, $P_-P_+$, such as those that contain the $eeZ$ or $eeγ$ vertex, have their polarization precision demands get more strict as positron polarization increases. We check the polarization precision demands for $10^{-3}$ on the Higgsstrahlung cross-section ($σ_{ZH}$) at 250~GeV, $10^{-4}$ on the electron left-right asymmetry ($A_{\rm e}$) at the Z pole, and $10^{-4}$ on the di-photon cross-section ($σ_{γγ}$) from the Z pole to 3~TeV. We find that, for measurements away from the Z pole, the goals can plausibly be attained if one can achieve precision on beam polarization better than $0.1\%$. For measu

Continuous-time Markov Chains are widely used to model stochastic dynamical systems, but key summary quantities such as means and covariances are often intractable. While Monte Carlo sampling provides asymptotically exact estimates, it becomes computationally prohibitive when moments must be evaluated across many parameter values. We develop a simulation-based surrogate modeling framework that learns parameter-to-moment mappings from Monte Carlo-derived, noise-corrupted training targets, enabling efficient and accurate approximation across the parameter space. We show that Monte Carlo noise affects mean estimation primarily through additive variance, whereas covariance estimation is additionally impacted by bias arising from nonlinear transformations of empirical estimates. Using a stochastic Susceptible-Infected-Recovered model, we demonstrate that neural networks accurately learn both mean and covariance under fixed simulation budgets allocated to constructing the noisy training labels. We further characterize how to allocate computational resources between parameter-space coverage and Monte Carlo replication, showing that covariance estimation requires a balanced allocation to c

In this work we investigate the relationship between kernel regularity and algorithmic performance in the bandit optimization of RKHS functions. While reproducing kernel Hilbert space (RKHS) methods traditionally rely on global kernel regressors, it is also common to use a smoothness-based approach that exploits local approximations. We show that these perspectives are deeply connected through the spectral properties of isotropic kernels. In particular, we characterize the Fourier spectra of the Matérn, square-exponential, rational-quadratic, $γ$-exponential, piecewise-polynomial, and Dirichlet kernels, and show that the decay rate determines asymptotic regret from both viewpoints. For kernelized bandit algorithms, spectral decay yields upper bounds on the maximum information gain, governing worst-case regret, while for smoothness-based methods, the same decay rates establish Hölder space embeddings and Besov space norm-equivalences, enabling local continuity analysis. These connections show that kernel-based and locally adaptive algorithms can be analyzed within a unified framework. This allows us to derive explicit regret bounds for each kernel family, obtaining novel results in

The discovery of genetic risk factors has transformed human genetics, yet the pace of new gene identification has slowed despite the exponential expansion of sequencing and biobank resources. Current approaches are optimized for the extremes of the allele frequency spectrum: rare, high-penetrance variants identified through burden testing, and common, low-effect variants mapped by genome-wide association studies. Between these extremes lies variants of intermediate frequency and effect size where statistical power is limited, pathogenicity is often misclassified, and gene discovery lags behind empirical evidence of heritable contribution. This 'missing middle' represents a critical blind spot across disease areas, from neurodevelopmental and psychiatric disorders to cancer and aging. In this review, we organize strategies for risk gene identification by variant frequency class, highlighting methodological strengths and constraints at each scale. We draw on lessons across fields to illustrate how innovations in variant annotation, joint modeling, phenotype refinement, and network-based inference can extend discovery into the intermediate range. By framing the frequency spectrum as a

In this paper we extend models of thermal conduction with phase transition from micro- to macro-scale. Such models were previously developed for soils in permafrost regions from pore to Darcy scale, and the Darcy scale models compare well to empirical relationships. The new general model blends soil model with bulk water model and thus works well for the soils with macro-pores; it also applies to new context including modeling thermal conduction in the snow and in cryoconite, and it is consistent with rigorous thermodynamics derivations as well as with practical models from the literature. From mathematical point of view, the general model relies on carefully defined relationships between temperature, phase fraction and internal energy, which we show are invertible in a properly defined framework of multivalued graphs. We also discuss and test practical models for average heat conductivity. Our framework allows to create monolithic numerical models useful for modeling of coupled soil and snow models as well as cryoconite. We motivate and illustrate the results with practical examples and computations.

Stellar streams have proven to be powerful tools for measuring the Milky Way's gravitational potential and hence its dark matter halo. In the coming years, Vera Rubin, Euclid, ARRAKIHS, and NGRST will uncover a plethora of streams around external galaxies. Although great in number, observations of these distant streams will often be limited to only the on-sky position of the stream. In this work, we explore how well we will be able to measure the dark matter haloes of these galaxies by fitting simplified mock streams with a variety of intrinsic and orbital properties in a range of data availability scenarios. We find that streams with multiple wraps around their host galaxy can constrain the overall radial profile and scale radius of the potential without radial velocities. In many other cases, a single radial velocity measurement often provides a significant boost to constraining power for the radial profile, scale radius, and enclosed mass of the dark matter halo. Given the wealth of data expected soon, this suggests that we will be able to measure the dark matter haloes of a statistically significant sample of galaxies with stellar streams in the coming years.

Close-in planets smaller than Neptune form two distinct populations composed of rocky super-Earths and sub-Neptunes that may host primordial H/He envelopes. The origin of the radius valley separating these two planet populations remains an open question and has been posited to emerge either directly from the planet formation process or via subsequent atmospheric escape. Multi-transiting systems that span the radius valley are known to be useful diagnostics of XUV-driven mass loss. Here, we extend this framework to test XUV-driven photoevaporation, core-powered mass loss, and an accretion-limited primordial radius valley model. Focusing on multi-transiting systems allows us to eliminate unobservable quantities that are shared within individual systems such as stellar XUV luminosity histories and the properties of the protoplanetary disk. We test each proposed radius valley emergence mechanism on all 221 known multi-transiting systems and calculate the minimum masses of the systems' enveloped planets to be consistent with the models. We compare our model predictions to 75 systems with measured masses and find that the majority of systems can be explained by any of the three proposed

Continuous attractor networks (CANs) are widely used to model how the brain temporarily retains continuous behavioural variables via persistent recurrent activity, such as an animal's position in an environment. However, this memory mechanism is very sensitive to even small imperfections, such as noise or heterogeneity, which are both common in biological systems. Previous work has shown that discretising the continuum into a finite set of discrete attractor states provides robustness to these imperfections, but necessarily reduces the resolution of the represented variable, creating a dilemma between stability and resolution. We show that this stability-resolution dilemma is most severe for CANs using unimodal bump-like codes, as in traditional models. To overcome this, we investigate sparse binary distributed codes based on random feature embeddings, in which neurons have spatially-periodic receptive fields. We demonstrate theoretically and with simulations that such grid-cell-like codes enable CANs to achieve both high stability and high resolution simultaneously. The model extends to embedding arbitrary nonlinear manifolds into a CAN, such as spheres or tori, and generalises li