Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive 3D density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a wind tunnel. We call this method WindDensity-MBIR and apply it using simulated data to difficult reconstruction scenarios with sparse data, small projection field of view, and limited angular extent. WindDensity-MBIR can recover high-order features in these scenarios within 10% to 25% error even when the tip, tilt, and piston are removed from the wavefront measurements.

Strongly lensed supernovae (SNe) provide a powerful way to study cosmology, SNe and galaxies. Modelling the lens system is key to extracting astrophysical and cosmological information. We present adaptive-optics-assisted high-resolution images of SN Winny (SN 2025wny) in the J and K filters obtained with the Large Binocular Telescope (LBT). The LBT imaging confirms the presence of a fifth point source, whose colour is consistent with that of the other SN images at similar phases, while lens modelling robustly supports its interpretation as an additional image of SN~Winny. We measure the positions of the five SN images with uncertainties varying between 1 and 14 milliarcseconds. We build the first mass models using lenstronomy and GLEE, and explore three classes of mass models for the two lens galaxies G1 and G2. The optimal model class of the three is a singular isothermal ellipsoid for G1, a singular isothermal sphere for G2, and an external shear. We infer the enclosed masses within the Einstein radius as 4.61^{+0.06}_{-0.04} \times 10^{11}\,M_\odot for G1 and 1.01\pm0.02 \times 10^{11}\,M_\odot for G2. The lensing configuration by the two lens galaxies can produce two additional magnified SN images beyond the five observed ones; the exclusion of such model configurations further constrains the lens model parameters. Our model fits to the observed image positions with an RMS of ~0.0012" - 0.0025", within the observed positional uncertainties. The predicted magnifications of the multiple images vary between ~1.6 (for the faintest fifth image E) to ~10 (for the brightest image A). The predicted relative lensing magnifications of the multiple images do not match that of the observed within 2σuncertainties. The differences in the relative magnifications could be due to millilensing/microlensing. Our mass models form the basis for future analyses of this unique system. (abridged)

In principle, machine learning (ML) can be used to obtain any electronic property of a many-body system from its electron density within density functional theory. However, some physical quantities are highly sensitive to small variations in the density. This 'ill-conditioning' limits the accuracy with which these quantities can be learned as density functionals from a fixed amount of data. We identify sources of ill-conditioning present in density functionals that belong to two ubiquitous classes: 1) Physical quantities that are globally gauge-dependent, meaning they change value if a constant shift is applied to the external potential -- for example, the total energy; 2) Functionals of the N-electron density that have an implicit dependence on the (N+1)-electron density, such as the fundamental gap. We demonstrate that widely used ML models exhibit orders-of-magnitude greater error when applied to these ill-conditioned density functionals compared to other functionals that fall into neither class, even when the global gauge is fixed to prevent constant shifts. Owing to an absence of ill-conditioning in potential functionals, we find that providing the external potential as input to the ML model leads to significantly improved predictions of quantities in these two classes.

A major public health issue is the growing resistance of bacteria to antibiotics. An important part of the needed response is the discovery and development of new antimicrobial strategies. These require the screening of potential new drugs, typically accomplished using high-throughput screening (HTS). Traditionally, HTS is performed by examining one compound per well, but a more efficient strategy pools multiple compounds per well. In this work, we study several recently proposed pooling construction methods, as well as a variety of pooled high-throughput screening analysis methods, in order to provide guidance to practitioners on which methods to use. This is done in the context of an application of the methods to the search for new drugs to combat bacterial infection. We discuss both an extensive pilot study as well as a small screening campaign, and highlight both the successes and challenges of the pooling approach.

We extend the functional Breuer-Major theorem by Nourdin and Nualart (2020) to the space of rough paths. The proof of tightness combines the multiplication formula for iterated Malliavin divergences, due to Furlan and Gubinelli (2019), with Meyer's inequality and a Kolmogorov-type criterion for the r-variation of cadlag rough paths, due to Chevyrev et al. (2022). Since martingale techniques do not apply, we obtain the convergence of the finite-dimensional distributions through a bespoke version of Slutsky's lemma: First, we overcome the lack of hypercontractivity by an iterated integration-by-parts scheme which reduces the remaining analysis to finite Wiener chaos; crucially, this argument relies on Malliavin differentiability of the nonlinearity but not on chaos decay and, as a consequence, encompasses the centred absolute value function. Second, in the spirit of the law of large numbers, we show that the diagonal of the second-order process converges to an explicit symmetric correction term. Finally, we compute all the moments of the remaining process and, through a fine combinatorial analysis, show that they converge to those of the Stratonovich Brownian rough path perturbed by an antisymmetric area correction, as computed by a suitable amendment of Fawcett's theorem. All of these steps benefit from a major combinatorial reduction that is implied by the original argument of Breuer and Major (1983).

The distribution of meteor magnitudes is known to follow an exponential distribution, where the base of this distribution is called the population index. The distribution of observed magnitudes preserves this behavior, but is truncated by the detection threshold. If both the population index and detection threshold can be determined, observed meteor rates can be converted to fluxes and extrapolated to any desired brightness or size. We argue that the distribution of observed or instrumental meteor magnitudes is best modeled as an exponentially modified Gaussian (exGaussian) distribution. This is for three reasons: first, an exGaussian distribution is the natural result of random variations in detection threshold and/or post-detection measurement errors in magnitude. Second, an exGaussian distribution provides a better fit to the magnitude distribution than all other competing distributions in the literature; we demonstrate this using both a set of faint optical meteor magnitudes and a set of radar meteor echo amplitudes. Finally, the population index, mean detection threshold, and random variation/error terms are easily extracted from the best-fit parameters of an exGaussian distribution.

As quantum technologies mature, telecommunication operators have a clear opportunity to unlock and scale new services by providing the connectivity layer that links quantum computers, sensors, clocks, and other quantum devices. Realizing this opportunity requires demonstrating quantum networking protocols, including quantum teleportation, under real-world conditions on existing telecom infrastructure. In this work, we demonstrate quantum teleportation over Deutsche Telekom's metropolitan fiber testbed in Berlin using commercial components deployed at the telecom datacenter. A local Bell-state measurement between 795 nm photons from a weak coherent source and from a bichromatic warm-atom entangled photon source enables conditional state transfer onto an O-band photon, which is transmitted through a 30-km field-deployed fiber loop under real-world environmental conditions. The teleported state is reconstructed after propagation via state tomography, achieving an average teleportation fidelity of 90\% on the deployed link. System performance is evaluated in both the absence and the presence of co-propagating C-band classical traffic within the same fiber, demonstrating compatibility with wavelength-division multiplexed telecom infrastructure carrying live data channels.

Generalized Sliced Inverse Regression (GSIR) is one of the most important methods for nonlinear sufficient dimension reduction. As shown in Li and Song (2017), it enjoys a convergence rate that is independent of the dimension of the predictor, thus avoiding the curse of dimensionality. In this paper we establish an improved convergence rate of GSIR under additional mild eigenvalue decay rate and smoothness conditions. Our convergence rate can be made arbitrarily close to $n^{-1/3}$ under appropriate decay rate and smoothness parameters. As a comparison, the rate of Li and Song (2017) is $n^{-1/4}$ under the best conditions. This improvement is significant because, for example, in a semiparametric estimation problem involving an infinite-dimensional nuisance parameter, the convergence rate of the estimator of the nuisance parameter is often required to be faster than $n^{-1/4}$ to guarantee desired semiparametric properties such as asymptotic efficiency. This can be achieved by the improved convergence rate, but not by the original rate. The sharpened convergence rate can also be established for GSIR in more general settings, such as functional sufficient dimension reduction.

A signed tree model of a graph $G$ is a compact binary structure consisting of a rooted binary tree whose leaves are bijectively mapped to the vertices of $G$, together with 2-colored edges $xy$, called transversal pairs, interpreted as bicliques or anti-bicliques whose sides are the leaves of the subtrees rooted at $x$ and at $y$. We design an algorithm that, given such a representation of an $n$-vertex graph $G$ with $p$ transversal pairs and a source $v \in V(G)$, computes a shortest-path tree rooted at $v$ in $G$ in time $O(p \log n)$. A wide variety of graph classes are such that for all $n$, their $n$-vertex graphs admit signed tree models with $O(n)$ transversal pairs: for instance, those of bounded symmetric difference, more generally of bounded sd-degeneracy, as well as interval graphs. As applications of our Single-Source Shortest Path algorithm and new techniques, we - improve the runtime of the fixed-parameter algorithm for first-order model checking on graphs given with a witness of low merge-width from cubic [Dreier and Toruńczyk, STOC '25] to quadratic; - give an $O(n^2 \log n)$-time algorithm for All-Pairs Shortest Path (APSP) on graphs given with a witness of low merge-width, generalizing a result known on twin-width [Twin-Width III, SICOMP '24]; - extend and simplify an $O(n^2 \log n)$-time algorithm for multiplying two $n \times n$ matrices $A, B$ of bounded twin-width in [Twin-Width V, STACS '23]: now $A$ solely has to be an adjacency matrix of a graph of bounded twin-width and $B$ can be arbitrary; - give an $O(n^2 \log^2 n)$-time algorithm for APSP on graphs of bounded twin-width, bypassing the need for contraction sequences in [Twin-Width III, SICOMP '24; Bannach et al. STACS '24]; - give an $O(n^{7/3} \log^2 n)$-time algorithm for APSP on graphs of symmetric difference $O(n^{1/3})$.

We extend the classical edge-triangle Exponential Random Graph Model (ERGM) to an inhomogeneous setting in which vertices carry types determined by an underlying partition. This leads to a block-structured ERGM where interaction parameters depend on vertex types. We establish a large deviation principle for the associated sequence of measures and derive the corresponding variational formula for the limiting free energy. In the ferromagnetic regime, where the parameters governing triangle densities are nonnegative, we reduce the variational problem to a scalar optimization problem, thereby identifying the natural block counterpart of the replica symmetric regime. Under additional restrictions on the parameters, comparable to the classical Dobrushin's uniqueness region, we prove uniqueness of the maximizer and derive a law of large numbers for the edge density.

We present a conservative extension ICaTT of the dependent type theory CaTT for weak $ω$-categories with a type witnessing coinductive invertibility of cells. This extension allows for a concise description of the "walking equivalence" as a context, and of a set of maps characterising $ω$-equifibrations as substitutions. We provide an implementation of our theory, which we use to formalise basic properties of invertible cells. These properties allow us to give semantics of ICaTT in marked weak $ω$-categories, building a fibrant marked $ω$-category out of every model of ICaTT.

Let $Z'\subset \mathbb{P}^{n}$ be a smooth projective hypersurface of degree $d>1$ and let $Z\to \mathbb{P}^n$ be the $μ_d$-cover totally ramified along $Z'$. We relate full level $d$ structures on the primitive cohomology $Z'$ with full level $d$ structures on the primitive cohomology of $Z$. In the special case, $d=n=3$ this makes a marking of a smooth cubic surface determine a level $3$-structure on the associated cubic threefold, thereby answering a question by Beauville. We expect many more such applications.

We investigate universal entanglement signatures of mixed-state phases obtained by decohering pure-state topological order (TO), focusing on topological corrections to logarithmic entanglement negativity and mutual information: topological entanglement negativity (TEN) and topological mutual information (TMI). For Abelian TOs under decoherence, we develop a replica field-theory framework based on a doubled-state construction that relates TEN and TMI to the quantum dimensions of domain-wall defects between decoherence-induced topological boundary conditions, yielding general expressions in the strong-decoherence regime. We further compute TEN and TMI exactly for decohered $G$-graded string-net states, including cases with non-Abelian anyons. We interpret the results within the strong one-form-symmetry framework for mixed-state TOs: TMI probes the total quantum dimension of the emergent premodular anyon theory, whereas TEN detects only its modular part.

In this paper we investigate the probability distribution of the sum $Y$ of $\ell$ independent identically distributed random variables taking values in $\mathbb{Z}_p$. Our main focus is the regime of small values of $\ell$, which is less explored compared to the asymptotic case $\ell \to \infty$. Starting with the case $\ell=3$, we prove that if the distributions of the $Y_i$ are uniformly bounded by $λ< 1$ and $p > 2/λ$, then there exists a constant $C_{3,λ} < 1$ such that \[ \max_{x \in \mathbb{Z}_p} \mathbb{P}[Y = x] \leq C_{3,λ}λ. \] Moreover, when the distributions are uniformly separated from $1$, the constant $C_{3,λ}$ can be made explicit. By iterating this argument, we obtain effective anticoncentration bounds for larger values of $\ell$, yielding nontrivial estimates already in small and moderate regimes where asymptotic results do not apply.

This paper addresses optimal design problems governed by multi-state stationary diffusion equations, aiming at the simultaneous optimization of the domain shape and the distribution of two isotropic materials in prescribed proportions. Existence of generalized solutions is established via a hybrid approach combining homogenization-based relaxation in the interior with suitable restrictions on admissible domains. Based on this framework, we propose a numerical method that integrates homogenization and shape optimization. The domain boundary is evolved using a level set method driven by the shape derivative, while the interior material distribution is updated via an optimality criteria algorithm. The approach is demonstrated on a representative example.

In this paper, the adaptive numerical solution of a 2D Poisson model problem by Crouzeix-Raviart elements ($\operatorname*{CR}_{k}$ $\operatorname*{FEM}$) of arbitrary odd degree $k\geq1$ is investigated. The analysis is based on an established, abstract theoretical framework: the \textit{axioms of adaptivity} imply optimal convergence rates for the adaptive algorithm induced by a residual-type a posteriori error estimator. Here, we introduce the error estimator for the $\operatorname*{CR}_{k}$ $\operatorname*{FEM}$ discretization and our main theoretical result is the proof ot Axiom 3: \textit{discrete reliability}. This generalizes results for adaptive lowest order $\operatorname*{CR}_{1}$ $\operatorname*{FEM}$ in the literature. For this analysis, we introduce and analyze new local quasi-interpolation operators for $\operatorname*{CR}_{k}$ $\operatorname*{FEM}$ which are key for our proof of discrete reliability. We present the results of numerical experiments for the adaptive version of $\operatorname*{CR}_{k}$ $\operatorname*{FEM}$ for some low and higher (odd) degrees $k\geq1$ which illustrate the optimal convergence rates for all considered values of $k$.

Developing effective control strategies for behind-the-meter energy storage to coordinate peak shaving and stacked services is essential for reducing electricity costs and extending battery lifetime in commercial buildings. This work proposes an end-to-end, two-stage framework for coordinating peak shaving and energy arbitrage with a theoretical decomposition guarantee. In the first stage, a non-parametric kernel regression model constructs state-of-charge trajectory bounds from historical data that satisfy peak-shaving requirements. The second stage utilizes the remaining capacity for energy arbitrage via a transfer learning method. Case studies using New York City commercial building demand data show that our method achieves a 1.3 times improvement in performance over the state-of-the-art forecast-based method, achieving cost savings and effective peak management without relying on predictions.

There is growing evidence that independently trained AI systems come to represent the world in the same way. In other words, independently trained embeddings from text, vision, audio, and neural signals share an underlying geometry. We call this the Representational Alignment Hypothesis (RAH) and investigate evidence for and consequences of this claim. The evidence is of two kinds: (i) internal structure comparison techniques, such as representational similarity analysis and topological data analysis, reveal matching relational patterns across modalities without explicit mapping; and (ii) methods based on cross-modal embedding alignment, which learn mappings between representation spaces, show that simple linear transformations can bring different embedding spaces into close correspondence, suggesting near-isomorphism. Taken together, the evidence suggests that, even after controlling for trivial commonalities inherent in standard data preprocessing and embedding procedures, a robust structural correspondence persists, hinting at an underlying organizational principle. Some have argued that this result shows that the shared structure is getting at a fundamental, Platonic level of reality. We argue that this conclusion is unjustified. Moreover, we aim to give the idea an alternative philosophical home, rooted in contemporary metasemantics (i.e., theories of what makes a representation and what makes something meaningful) and responses to the symbol grounding problem. We conclude by considering the scope of the RAH and proposing new ways of distinguishing semantic structures that are genuinely invariant from those that inevitably arise due to the fact that all our data is generated under human-specific conditions on Earth.

Low physical activity is a known risk factor for major depressive disorder (MDD), but changes in activity before a first clinical diagnosis remain unclear, especially using long-term objective measurements. This study characterized trajectories of wearable-measured physical activity during the year preceding incident MDD diagnosis. We conducted a retrospective nested case-control study using linked electronic health record and Fitbit data from the All of Us Research Program. Adults with at least 6 months of valid wearable data in the year before diagnosis were eligible. Incident MDD cases were matched to controls on age, sex, body mass index, and index time (up to four controls per case). Daily step counts and moderate-to-vigorous physical activity (MVPA) were aggregated into monthly averages. Linear mixed-effects models compared trajectories from 12 months before diagnosis to diagnosis. Within cases, contrasts identified when activity first significantly deviated from levels 12 months prior. The cohort included 4,104 participants (829 cases and 3,275 controls; 81.7% women; median age 48.4 years). Compared with controls, cases showed consistently lower activity and significant downward trajectories in both step counts and MVPA during the year before diagnosis (P < 0.001). Significant declines appeared about 4 months before diagnosis for step counts and 5 months for MVPA. Exploratory analyses suggested subgroup differences, including steeper declines in men, greater intensity reductions at older ages, and persistently low activity among individuals with obesity. Sustained within-person declines in physical activity emerged months before incident MDD diagnosis. Longitudinal wearable monitoring may provide early signals to support risk stratification and earlier intervention.

Higher-Form Symmetries (HFS) of a closed bosonic M2-brane formulated on a compactified target space $\mathcal{M}_9 \times T^2$ are investigated. We show that there is an obstruction to the gauging of these global symmetries in the presence of background fields, a mixed 't~Hooft anomaly. Its cancellation is obtained by the inflow term constructed in terms of gauge fields which are flat connections on a $U(1)$-principal bundle and a torsion $\mathcal{G}_1^{\nabla_c}$-gerbe on the M2-brane worldvolume. The effect of these gauge structures together with non trivial \textit{winding} embedding maps ensures the breaking of the continuous HFS $U(1)$ symmetry to a discrete subgroup and a worldvolume flux condition on the M2-brane. A Wilson surface, identified with the holonomy Hol$_\nabla$ one of the Gerbe structures, the flat $\mathcal{G}_1^{\nabla_c}$-gerbe, is naturally introduced as the topological operator characterizing the M2-brane. The resulting topological operators realize discrete symmetries associated with the \textit{winding} and the flux/\textit{monopole} sectors, and their operator algebra is well-defined: the \textit{monopole} operator acts non trivially on a \textit{vortex-dressed} operator, while the winding operator acts on the pullback of the Wilson surface.

We introduce and analyze a nonlocal generalization of Whittle--Matérn Gaussian fields in which the smoothness parameter varies in space through the fractional order, $s=s(x)\in[\underline{s}\,,\bar{s}]\subset(0,1)$. The model is defined via an integral-form operator whose kernel is constructed from the modified Bessel function of the second kind and whose local singularity is governed by the symmetric exponent $β(x,y)=(s(x)+s(y))/2$. This variable-order nonlocal formulation departs from the classical constant-order pseudodifferential setting and raises new analytic and numerical challenges. We develop a novel variational framework adapted to the kernel, prove existence and uniqueness of weak solutions on truncated bounded domains, and derive Sobolev regularity of the Gaussian (spectral) solution controlled by the minimal local order: realizations lie in $H^r(G)$ for every $r<2\underline{s}-\tfrac{d}{2}$ (here $H^r(G)$ denotes the Sobolev space on the bounded domain $G$), hence in $L_2(G)$ when $\underline s>d/4$. We also present a finite-element sampling method for the integral model, derive error estimates, and provide numerical experiments in one dimension that illustrate the impact of spatially varying smoothness on samples covariances. Computational aspects and directions for scalable implementations are discussed.

We extend recent algebro-geometric results for coupled cluster theory of quantum many-body systems to the truncation varieties arising from the doubles approximation (CCD), focusing on the first genuinely nonlinear doubles regime of four electrons. Since this doubles truncation variety does not coincide with previously studied varieties, we initiate a systematic investigation of its basic algebro-geometric invariants. Combining theoretical and numerical results, we show that for $4$ electrons on $n\leq 12$ orbitals, the CCD truncation variety is a complete intersection of degree $2^{\binom{n-4}{4}}$. Using representation-theoretic arguments, we uncover a Pfaffian structure governing the quadratic relations that define the truncation variety for any $n$, and show that an exact tensor product factorization holds in a distinguished limit of disconnected doubles. We connect these structural results to the computation of the beryllium insertion into molecular hydrogen ({Be$\cdots$H$_2$ $\to$ H--Be--H}), a small but challenging bond formation process where multiconfigurational effects become pronounced.

Active photonic integrated circuits (PICs) in the visible spectrum are essential for on-chip applications, requiring low-loss waveguides with broad transparency and efficient, low-power phase modulation. Here, we demonstrate a compact, ultra-low-power phase modulator based on a silicon nitride (Si$_3$N$_4$) waveguide integrated with thin-film lead zirconate titanate (PZT) that actuates a bridge-type MEMS. The suspended actuator exploits PZT's strong piezoelectric effect to induce mechanically driven phase shifts, enabling efficient modulation in a Mach--Zehnder interferometer. For 3~mm and 5~mm modulators, phase shifts of $1.45π$ and $2.5π$ are achieved at 10~V, corresponding to a scalability metric ($V_π\cdot L$) of 2.25~V$\cdot$cm at 635~nm. This represents an order-of-magnitude improvement in scalability over stress-optic PZT modulators. The devices also exhibit ultralow power consumption ($\sim 12\,\mathrm{nW}$), $\sim 5\,\mathrm{ms}$ rise time, and optical loss $< 0.75\,\mathrm{dB/cm}$. Furthermore, we demonstrate on-chip beam shaping.

In this paper we provide optimal bounds for fully discrete approximations to finite horizon problems via dynamic programming. We adapt the error analysis in \cite{nos} for the infinite horizon case to the finite horizon case. We prove an a priori bound of size $O(h+k)$ for the method, $h$ being the time discretization step and $k$ the spatial mesh size. Arguing with piecewise constants controls we are able to obtain first order of convergence in time and space under standard regularity assumptions, avoiding the more restrictive regularity assumptions on the controls required in \cite{nos}. We show that the loss in the rate of convergence in time of the infinite case (obtained arguing with piece-wise controls) can be avoided in the finite horizon case

This research paper proposes an efficient methodology for the allocation of multiple photovoltaic (PV)-based distributed generation (DG) units in the radial distribution network (RDN), while considering the loading capacity of the network. The proposed method is structured using a two-stage approach. In the first stage, the additional active power loading capacity of the network and each individual bus is determined using an iterative approach. This analysis quantifies the network's additional active loadability limits and identifies buses with high active power loading capacity, which are considered candidate nodes for the placement of DG units. Subsequently, in the second stage, the optimal locations and sizes of DG units are determined using the Monte Carlo method, with the objectives of minimizing voltage deviation and reducing active power losses in the network. The methodology is validated on the standard IEEE 33-bus RDN to determine the optimal locations and sizes of DG units. The results demonstrate that the optimal allocation of one, two, and three DG units, achieved from proposed method, reduces network active power losses by 50.37%, 58.62%, and 65.16%, respectively, and also significantly enhances the voltage profile across all buses. When the obtained results are compared with the results of several existing studies, it is found that the proposed method allows for larger DG capacities and maintains better voltage profiles throughout the RDN.

First-order phase transitions are commonly associated with a discontinuous behavior of some of the thermodynamic variables and the presence of a latent heat. In the present study it is shown that this is not necessarily the case. Using standard thermodynamics, the general characteristics of phase transitions are investigated, considering an arbitrary number of conserved particle species and coexisting phases, and an arbitrary set of state variables. It is found that there exist two different possible types of realizations of a phase transition. In the first type, one has the immediate replacement of a single phase with another one. As a consequence, some of the global extensive variables indeed behave discontinuously. In the second type, one has instead the gradual (dis-) appearance of a single phase over a range of the state variables. This leads to a continuous behavior of the (global) basic thermodynamic variables. Furthermore, in this case it is not possible to define a latent heat in a trivial manner. It is derived that the latter (former) case happens if the number of extensive state variables used is larger or equal (lower) than the number of coexisting phases. The choice of the state variables thus place a crucial role for the qualitative properties of the phase transition.

Illicit massage businesses (IMBs) masquerade as legitimate massage parlors while facilitating commercial sex and human trafficking. Law enforcement must identify these businesses within a dense population of lawful establishments, but investigative resources are limited and the illicit status of each location is unknown until inspection. Detection methods based on online reviews offer some insight, yet operators can manipulate these signals, leaving covert establishments undetected. IMBs constitute one of the largest segments of indoor sex trafficking in the United States, with an estimated 9,000 establishments. Mobility data offers an alternative to online signals, covering establishments that avoid digital visibility entirely. We derive features from mobility data spanning temporal visitation patterns, dwell times, visitor catchment areas, and demand stability. Because confirmed labels exist only for establishments identified through advertising platforms, we employ positive-unlabeled learning to address the label asymmetry in ground truth. The model achieves 0.97 AUC and 0.84 Average Precision. Four operational signatures characterize high-risk establishments: demand consistency, evening-concentrated visits, compressed service durations, and locally drawn clientele. The model produces risk scores for each business-week observation. Aggregating to the business level, prioritizing the highest-risk 10% of massage establishments captures 53% of known illicit operations, a 5.3-fold improvement over uninformed inspection. We develop a decision-support system that produces calibrated prioritization scores for law enforcement, enabling investigators to concentrate inspections on the highest-risk venues. The operational signatures may resist strategic manipulation because they reflect actual operations rather than online signals that operators can control.

We present a lattice-QCD determination of the $Λ\to p$ vector and axial-vector form factors, providing theoretical input for studies of the semileptonic decay $Λ\to p\ell\barν_\ell$. The calculation is carried out on a single gauge ensemble with physical light, strange, and charm quark masses and delivers a precise determination of the complete set of transition form factors, including second-class contributions. Using these form factors, we compute decay rates for both the electronic and muonic channels, as well as their ratio, which offers a sensitive test of lepton-flavor universality and possible non-standard scalar or tensor interactions. This decay mode provides a theoretically well-controlled avenue for extracting the CKM matrix element $|V_{us}|$ from the baryon sector. Our estimate of $|V_{us}|$ is obtained by combining our recent lattice-QCD results with recent measurements of the relevant branching fraction reported by BESIII and LHCb.

The potential for topological Kondo insulating behavior in d-electron systems has attracted interest in studying the surface states of the correlated insulators FeSb2 and FeSi. While detailed studies and theoretical description of a spin-orbit coupled ferromagnetic surface state have been applied to FeSi, the magnetic properties of the surface states of FeSb2 have not been addressed. Here, we report on the surface magnetic properties of FeSb2, utilizing the surface area dependence of magnetic susceptibility to separate the surface Curie-Weiss temperature dependence from the bulk spin-gap susceptibility. We use these results to further extract the surface magnetic anisotropy of a thin, rough-surfaced single-crystal FeSb2 to compare with the observed magnetotransport anisotropy, and find good agreement between the anisotropy in the surface magnetization and surface magnetotransport. We conclude with evidence of an anomalous Hall contribution to the low-temperature surface transport.

The emergence of agentic AI marks a new phase in the digital transformation of healthcare. Distinct from conventional generative AI, agentic AI systems are capable of autonomous, goal-directed actions and complex task coordination. They promise to support or even collaborate with clinicians and patients in increasingly independent ways. While agentic AI raises familiar moral concerns regarding safety, accountability, and bias, this article focuses on a less explored dimension: its capacity to transform the moral fabric of healthcare itself. Drawing on the framework of techno-moral change and the three domains of decision, relation and perception, we investigate how agentic AI might reshape the patient-physician relationship and reconfigure core concepts of medical morality. We argue that these shifts, while not fully predictable, demand ethical attention before widespread deployment. Ultimately, the paper calls for integrating ethical foresight into the design and use of agentic AI.