Despite recent advances in Computer Vision and Artificial Intelligence (AI), AI-assisted video solutions have struggled to penetrate real-world urban environments due to significant concerns regarding privacy, ethical risks, and technical challenges like bias and explainability. This work addresses these barriers through a case study in a city-center public market, demonstrating a pathway for the responsible deployment of AI in community spaces. By adopting a user-centric methodology that prioritizes public trust and privacy safeguards, we show that detailed, operationally relevant behavioral insights can be derived from abstract data representations without compromising ethical standards. The study focuses on generating Multi-Metric Behavioral Insights through the extraction of three complementary signals: customer directional flow, dwell duration, and movement patterns. Utilizing human pose detection and complex behavioral analysis - processed through geometric normalization and motion modeling - the system remains robust under tracking fragmentation and occlusion. Data collected over 18 days, spanning routine operations and a festival window from May 2-4, reveals a consistently right-skewed dwell-time behavior. While most visits last approximately 3-4 minutes, peak activity periods increase the mean to roughly 22 minutes. Furthermore, movement analysis indicates uneven circulation, with over 60% of traffic concentrated in approximately 30% of the venue space. By mapping popular thoroughfares and high-traffic storefronts, this case study provides venue managers and business owners with objective, measurable information to optimize foot traffic. Ultimately, these results demonstrate that AI-enabled video solutions can be successfully integrated into urban environments to provide high-fidelity spatial analytics while maintaining strict adherence to privacy and social responsibility.

Self-propelled active matter can exhibit vastly different behavior than systems with purely Brownian motion. In Eur. Phys. J. E 40, 23 (2017), Zeitz, Wolf, and Stark compared an active matter particle with a Brownian particle moving in a random obstacle array. They showed that near the obstacle percolation density, both Brownian and active particles exhibit the same subdiffusive behavior, but the active particle reaches a steady state more rapidly. They also found that for high activity, the active particle has a lower effective diffusion than the Brownian particle due to the increased self-trapping effect generated by the activity. This result opens new directions for the study of active matter in disordered media, including bacteria in porous media, active colloids on quenched disorder,and active particles in crowded environments.

Ultrafast photoexcitation is an emerging route to selective control of phase transitions. However, it is difficult to determine which modes govern the transformation and how effectively they are targeted by photoexcitation. This is exemplified in vanadium dioxide, which transitions from a monoclinic insulator to a rutile metal upon heating or photoexcitation. There is a long-standing debate about whether this transition is electronically or structurally driven and whether the structural component is coherent, driven by a single structural mode or thermal in nature. In this work, we develop a simple thermodynamic framework based on temperature-dependent ultrafast pump-probe measurements and contrast it to microscopic-detail-free modelling to identify the driving mechanism of the transition, revealing that population of the full thermal phonon spectrum, especially high-frequency oxygen modes, is necessary to stabilize the metallic phase. Our approach can straightforwardly be applied to determine the nature of other photoinduced phase transitions without the need for complex multi-messenger experiments and can guide new control strategies, even for incoherent transitions.

Tennenbaum's theorem states that PA does not admit any nonstandard computable model. In 2022, Pakhomov proved that this theorem is fragile in regards to how PA is expressed, by constructing a theory that is definitionally equivalent to PA (roughly: "it's PA but with a different choice of signature") for which there is a computable nonstandard model. He showed that this fragility does not extend to true arithmetic (any nonstandard model of a theory definitionally equivalent to $\mathrm{Th}(\mathbb{N})$ is not computable), but the question of whether this fragility extends to fragments of PA of intermediate strength was left open. We show that it does, by constructing a sequence of theories $T^n$ which are definitionally equivalent to: "PA plus all $Π^0_n$ truths", all of which admit computable nonstandard models. In the process, we produce a general-purpose theorem for strong jump inversion. Besides applying this theorem to obtain our novel result, we show that several known results from the literature can be seen as direct applications of our theorem.

Spectroscopy of Rydberg states has become a popular platform for quantum sensing, with the most common readout scheme being two-photon electromagnetically induced transparency (EIT) using counter-propagating laser beams. In this scheme, the energy resolution of the Rydberg state is set by the spectral linewidth of the EIT feature. While selection criteria for the two-photon resonance can narrow the linewidth to the order of the Rydberg state decay rate for a single atom, the Doppler shift from thermal velocity of the atoms broadens the ensemble linewidth to the order of the decay rate of the intermediate state. Here, we derive an analytic expression for the Doppler residual lineshape in the low-power limit and corroborate the results with experiment. For Rb, we find the full-width at half-maximum linewidth limit to be 1.84 MHz when scanning the coupling laser and measure an experimental linewidth of 2.04 MHz. These linewidths are around a factor of two narrower than previous theoretical estimates as well as previously reported measured linewidths. With this, we demonstrate the most precise two-photon energy resolution of a Rydberg state in thermal vapor to date. We then map out broadening mechanisms near this limit.

Motivated by an open question going back to P.Malliavin and P.-A.Meyer (and closely related to the foundational work of S.Watanabe) on whether Malliavin-Watanabe-Sobolev regularity admits a characterization in terms of a holomorphic Laplace image similar as for Hida distributions, we establish a characterization of the spaces $\mathcal{D}^{α,2}$ for all $α\in\mathbb{R}$ via the Bargmann-Segal norm of the $S$-transform. More precisely, we express $\mathcal{D}^{α,2}$-regularity, $α> 0$, of $F\in L^{2}(μ)$, as well as dual regularity of distributions, in terms of integrability, differentiability and growth properties of the function \[ (0,1) \ni λ\longmapsto \int_{\mathcal{S}'_{\mathbb{C}}} |SF(λu)|^{2}\,dν(u) \] involving integer-order derivatives in $λ$ for $α\in\mathbb{N}$ and Riemann-Liouville fractional derivatives/integrals for non-integer $α$. Here $ν$ is the Gaussian Bargmann-Segal measure. This yields practical criteria for both positive and negative (including fractional) orders of Malliavin regularity and thereby bridges Malliavin calculus and Bargmann-Segal techniques from white noise analysis. Applications are worked out for Donsker's delta, self-intersection local times of Gaussian processes, and Gauss kernels.

Accurate simulation of pulsar flux variability is critical for testing Square Kilometre Array (SKA) interferometric pipelines. However, most existing simulators neglect the effects of integration time and related observational parameters, limiting their realism and utility for interferometric end-to-end testing. To address these shortcomings, we develop a Pulsar Simulator for SKA-scale interferometric observations (PulSKASim), which models pulsar flux evolution across pulsar period, maximum flux, duty cycle, and noise, accounting for integration time, sampling, and observation duration, and naturally models flux smoothing that arises from finite integration within each dump time. PulSKASim generates synthetic measurement sets using functions in simulators for radio interferometers, such as OSKAR and Pyuvsim, where each snapshot contains pulsars with controlled flux levels, enabling realistic per-time-slot experiments. This simulator allows for detailed assessment of calibration, imaging, and detection pipelines under realistic SKA-like conditions, bridging pulsar variability modelling with interferometric simulation in a way not achievable by existing tools.

Waveguide Quantum Electrodynamics (WQED) offers a suitable stage for controlling the interaction of light with atoms, allowing for collective phenomena such as super- and subradiance. In a chiral waveguide setup, the quantum state evolves through all the Hilbert space, rendering an exact theoretical treatment exponentially hard and unobtained to date for more than $\sim 20$ atoms. In this work, we use a computationally efficient higher order mean-field approximation to model the radiation dynamics in a chirally coupled array of atoms, showing good agreement with recent experimental results. Further, based on a perturbative approximation of the full dynamics, we develop an analytical solution that captures photon statistics for a moderate atom number, $N$, and a homogeneous atom-waveguide coupling, $β$. Finally, we show that capturing the onset of second-order coherence from a fully inverted state requires a fourth-order mean-field approximation, as lower-order treatments fail to account for the necessary four-body correlations. These results illustrate the complex behavior of a symmetry-lacking system, and the methods discussed here provide systematic analytical solutions to which semi-classical methods such as the cumulant expansion or the truncated Wigner approximation can be benchmarked.

Cyber-Physical Systems (CPS) play a critical role in modern industrial domains, including manufacturing, energy, transportation, and healthcare, where they enable automation, optimization, and real-time decision-making. Ensuring the robustness of these systems is paramount, as failures can have significant economic, operational, and safety consequences. This paper present findings from an industrial survey conducted in Wallonia, covering a wide range of sectors, to assess the current state of practice in CPS robustness. It investigates robustness from how it is understood and applied in relationship with requirements engineering, system design, test execution, failure modes, and available tools. It identifies key challenges and gaps between industry practices and state-of-the-art methodologies. Additionally, it compares our findings with similar industrial surveys from the literature.

Scaling up quantum algorithms to tackle high-impact problems in science and industry requires quantum error correction and fault tolerance. While progress has been made in experimentally realizing error-corrected primitives, the end-to-end execution of logical quantum algorithms using only fault-tolerant (FT) components has remained out of reach. We demonstrate the FT and error-corrected execution of two quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA) and the Harrow-Hassidim-Lloyd (HHL) algorithm applied to the Poisson equation, on Quantinuum H2 and Helios trapped-ion quantum processors using the $[[7,1,3]]$ Steane code. For QAOA circuits on 5 and 6 logical qubits, we show performance improvements from increasing the number of QAOA layers and the number of $T$ gates used to approximate logical rotations, despite increased physical circuit complexity. We further show that QAOA circuits with up to 8 logical qubits and 9 logical $T$ gates perform similarly to unencoded circuits. For the largest QAOA circuits we run, with 12 logical (97 physical) qubits and 2132 physical two-qubit gates, we still observe better-than-random performance. Finally, we show that adding active QEC cycles and increasing the repeat-until-success limit of state preparation subroutines can improve the performance of a quantum algorithm, thereby demonstrating critical capabilities of scalable FT quantum computation. Our results are enabled by an FT logical $T$ gate implementation with an infidelity of $\sim 2.6(4)\times10^{-3}$ and dynamic circuits with measurement-dependent feedback. Our work demonstrates near-break-even performance of complex, error-corrected algorithmic quantum circuits using only FT components.

Graph processing at scale presents many challenges, including the irregular structure of graphs, the latency-bound nature of graph algorithms, and the overhead associated with distributed execution. While existing frameworks such as Spark GraphX and the Parallel Boost Graph Library (PBGL) have introduced abstractions for distributed graph processing, they continue to struggle with inherent issues like load imbalance and synchronization overhead. In this work, we present a distributed library prototype and a distributed implementation of three key graph algorithms - Breadth-First Search (BFS), PageRank, and Triangle Counting, using C++ mechanisms from the NWgraph library and leveraging HPX's distributed containers and asynchronous constructs. These algorithms span the categories of Traversal, centrality, and Pattern matching, and are selected to represent diverse computational characteristics. We evaluate our HPX-based implementations against GraphX, and PBGL, showing that a high-performance runtime such as HPX enables the construction of algorithms that significantly outperform conventional frameworks by exploiting asynchronous execution, latency hiding, and fine-grained parallelism in shared memory. All algorithms in our prototype follow a unified execution model in which local and remote computations are expressed using the same programming abstractions, with asynchrony managed transparently by the runtime. This design explicitly leverages shared-memory parallelism within each locality while overlapping communication and computation across localities, providing a practical foundation for extending this approach to a broader class of distributed graph algorithms.

Entanglement generated by Spontaneous Parametric Down Conversion (SPDC) involves multiple, often mutually correlated degrees of freedom. These degrees of freedom are often treated independently, overlooking the intrinsic correlation between them. We focus on the spatial spectral correlations that, if left uncontrolled, introduce distinguishability and reduce coherence, undermining applications such as high-dimensional OAM encoding. We analyze the spatio spectral structure of the biphoton and identify source configurations enabling a strong reduction of such correlations. We then quantify how spatial spectral coupling degrades OAM spatial purity, mapping high-purity regions as functions of OAM order, crystal length, and pump/collection waists. The resulting design parameters enable engineering bright, high purity OAM entangled sources, reducing the need for loss-introducing filtering and therefore supporting scalable high-dimensional photonic quantum technologies.

Double-peaked (DP) broad emission line profiles in active galactic nuclei (AGNs) are often interpreted as signatures of rotating disk-like structures in the broad-line region (BLR) and are commonly observed in low-luminosity AGNs using recombination lines. We use optical spectroscopy to investigate the origin of double-peaked broad emission line profiles observed not only in hydrogen recombination lines but also in forbidden transitions in the LINER galaxy IC 1459. We detected DP emission in all strong optical lines except for the [S II] doublet, which has the lowest critical density among all the lines. We successfully fitted the DP broad profiles using a disk-like BLR model, assuming a circular accretion disk with an inclination of approximately 35 degrees and internal turbulence of about 500 km/s, confined within a maximum radius of 9.6 (+4.8, -1.1) light-years. We estimate a full width at half maximum of the DP profiles of about 3300 km/s. Our results provide new insights into the structure of the BLR, indicating that forbidden emission lines can be produced in lower-density regions near the outskirts of the BLR.

Survey sampling is concerned with the estimation of finite population parameters. In practice, survey data suffer from item nonresponse, which is commonly handled through imputation, i.e., replacing missing values with predicted values. As a result, the properties of the resulting imputed estimator depend critically on the properties of the prediction method used. In turn, prediction methods themselves depend on the choice of variables and tuning parameters used to fit the imputation model. In this article, we study the problem of variable selection for linear regression imputation. Although variable selection has been widely studied across many fields, primarily for identification or prediction, its role in imputation for survey data has received comparatively little attention. We introduce the notion of an optimal imputation model defined through an oracle loss function and show that, with probability tending to one, the optimal model coincides with the true model. We also examine the consequences of using misspecified models -- either omitting relevant covariates or including irrelevant ones -- on consistency and asymptotic variance. We then develop a complete methodological framework for constructing confidence intervals after model selection. The proposed confidence intervals are shown to be asymptotically valid and optimal among all candidate models. Simulation studies indicate that the proposed methodology performs well in finite samples.

Accurate prediction of metal-oxide adhesion in high-entropy alloys (HEAs) is challenging because interfacial segregation, atomic environments, and macroscopic thermodynamic quantities are strongly correlated. Relying solely on first-principles approaches is too expensive for exploring composition, solute concentration, and co-segregation effects. To address this, we extend the macroscopic atom model (MAM) for multicomponent alloys using composition-consistent surface fractions and an interfacial pair-probability formalism that captures deviations from random contact statistics. Applied to CoCrFeNi (AlCoCrFeNi) HEA in contact with Cr2O3 (Al2O3), the model predicts segregation energies and work of separation as continuous functions of composition, reproducing the correct segregation hierarchy of Hf, Y, Zr, and S. The stronger segregation tendency at Al2O3 interfaces, and the non-linear dependence of surface energy and adhesion on solute content and co-segregation is also captured. The results are benchmarked with DFT calculations, which shows consistent trends, particularly the strengthening of adhesion by Hf and Zr through strong metal-oxygen bonding and the weakening effect of S. These results demonstrate that the extended MAM provides a physically interpretable, computationally efficient, and quantitatively predictive framework for screening segregation-controlled adhesion beyond the limits of DFT.

The time delay (or Sliding Window) embedding is a technique from dynamical systems to reconstruct attractors from time series data. Recently, descriptors from Topological Data Analysis (TDA) -- specifically, persistence diagrams -- have been used to measure the shape of said reconstructed attractors in applications including periodicity and quasiperiodicity quantification. Despite their utility, the fast computation of persistence diagrams of sliding window embeddings is still poorly understood. In this work, we present theoretical and computational schemes to approximate the persistence diagrams of sliding window embeddings from quasiperiodic functions. We do so by combining the Three Gap Theorem from number theory with the Persistent Künneth formula from TDA, and derive fast and provably correct persistent homology approximations. The input to our procedure is the spectrum of the signal, and we provide numerical as well as theoretical evidence of its utility to capture the shape of toroidal attractors.

The Mpemba effect is a thermodynamic anomaly in which a system farther away in temperature from equilibrium thermalizes before one that is initially closer. The effect has been experimentally observed across a wide range of systems, including water, colloids, and trapped ions. It has recently been the focus of numerous studies aimed at understanding its mechanisms and developing multiple applications. Despite extensive work in the field, clearly determining which types of systems exhibit the Mpemba effect remains an open question. To address this, we derive simple necessary conditions on the transition rates for the Mpemba effect in a Markovian 3-level system and show that they can be applied to study the Mpemba effect in an N-level system. Multiple time scales govern thermalization in these systems. This allows the evolution to occur more quickly across larger temperature differences, explaining the Mpemba effect. We apply our protocol to evaluate which types of systems exhibit the Mpemba effect and, in doing so, explain why the Mpemba effect in Markovian systems remains a thermodynamic anomaly. In particular, due to the maximum entropy principle, our conditions allow us to discard the sub-Ohmic and Ohmic spectra. The latter describes a wide range of physical and chemical phenomena, which will not exhibit the Mpemba effect. Moreover, our results provide a clear path to determine the minimal physical requirements for the Mpemba effect, and we apply them to understand its underlying mechanisms better. Finally, our protocol could help identify relevant parameters for experiments, numerical simulations and diverse applications.

Semi- and quasi-classical (SC) theories can handle arbitrary interatomic interactions and are thus well-suited to predict quantum dynamics in condensed phases that encode energy and charge transport, spectroscopic responses, and chemical reactivity. However, SC theories can be computationally expensive and inaccurate. When combined with generalized quantum master equations (GQMEs), the resulting SC-GQMEs have been observed to enhance the efficiency and accuracy of SC dynamics. Yet, while the mechanism responsible for improved efficiency is clear, the underlying improved accuracy remains elusive. What is worse, SC-GQMEs can yield unphysical dynamics in challenging parameter regimes -- a shortcoming that might be avoided if the mechanism of accuracy improvement were understood. Here, we uncover this mechanism. We leverage short-time analyses to prove that exact, "left-handed" time-derivatives delay the onset of SC inaccuracy, and show that their numerical integration yields dynamics with improved accuracy, even without the GQME. We find, however, that these derivatives are a double-edged sword: while offering greater short-time accuracy, they become unphysical in challenging parameter regimes. Because short-lived memory kernels can leverage short-time accuracy while circumventing long-time instability, we develop a protocol to unambiguously determine the memory kernel cutoff, even in challenging regimes where previous treatments had failed. Our insights into accuracy improvement and kernel cutoff protocol can be expected to apply to complex systems that go beyond simple models.

In this paper, we derive characteristic identities for the split Casimir operator of the Lie algebra $so(2r)$ in tensor products of spinor representations of the same and opposite chiralities. Using these identities, we explicitly construct projectors onto invariant subspaces of this operator and compute their traces. The results obtained allow us to derive explicit expressions for the colour factors of ladder Feynman diagrams in gauge theories with gauge group $Spin(2r)$. In addition, we obtain a new form of a solution to the Yang-Baxter equation that is invariant under the action of the Lie algebra $so(2r)$ in spinor representations.

Extragalactic nuclear transients that exhibit repeating outbursts can be modeled as the repeated dynamical interaction between bound stars and supermassive black holes (SMBHs). A subset of these transients, with recurrence timescales of months-to-years, have been explained as accretion flares from the repeated tidal stripping of a star by an SMBH, in a repeating partial tidal disruption event (rpTDE). We outline the scope of the rpTDE model and discuss hydrodynamical simulations and analytical predictions for the stability of stars undergoing repeated mass loss, and the long-term evolution of these flares as a function of stellar type and orbital parameters. Our findings demonstrate that high-mass and centrally concentrated stars undergo negligible changes in structure in response to small amounts ($\sim 1-10\% M_\star$) of mass loss, and can survive many mass-stripping encounters with an SMBH. Contrarily, low-mass and less evolved stars are unstable to mass loss, and would be destroyed within a few orbits. We discuss the implications of these results for constraining the stellar type and orbital parameters of observed sources, such as ASASSN-14ko, for which $\gtrsim 20$ flares have been observed, and AT2020vdq, which exhibits a second flare that is brighter than its primary outburst.

Atomic frequency standards have achieved steadily increasing precision over the past seventy years, enabled in part by feedback mechanisms that stabilise their output. In parallel, the timekeeping capabilities of quantum systems have been explored within the recently developed ticking-clock framework, which models clocks as dynamical systems producing a stochastic sequence of ticks. However, a theoretical description that unifies these perspectives and incorporates feedback into autonomous quantum clocks has been lacking. We introduce a framework for feedback-controlled clockworks in which classical information extracted from the tick sequence is used to influence the subsequent dynamics of the clock. We show that such feedback preserves the core structural features of self-timing and clockwork independence that characterise autonomous ticking clocks. We further identify the signal-to-noise ratio $\mathfrak{S}$ as the fundamental figure of merit for assessing the performance of feedback-controlled clocks. Applying our framework to two representative architectures, we prove that classical clockworks cannot surpass the optimal signal-to-noise ratio achievable without feedback. In contrast, for quantum clockworks we present numerical evidence that feedback can provide a genuine performance enhancement, improving the maximal attainable signal-to-noise ratio. These results establish feedback as a potentially essential ingredient in pushing the fundamental limits of timekeeping in the quantum regime.

Tidal interactions in close stellar binaries are central to their orbital and rotational evolution, making observational tests of theoretical predictions essential for our understanding of the evolution of these, as well as close exoplanetary systems. Such tests require precise measurements of the orbital eccentricity and stellar rotation. The EBLM (Eclipsing Binary Low Mass) survey delivers a homogeneous sample of eclipsing binaries, composed of F/G/K primaries and M-dwarf (or low-mass K-dwarf) secondaries. We analyze 68 unequal mass binaries ($0.1 \leq q \leq 0.6$, where $q$ is the mass ratio), with measurable primary star rotation rates from TESS, and over a decade of radial velocity observations. This sample probes the critical regime where tidal effects are expected to transition between being efficient and inefficient. We find that ~75% of our sample has circularized, with eccentric systems confined to $P_{\rm orb} \gtrsim 3$ days, with modest eccentricities (e < 0.25). Roughly ~78% of our sample is synchronized, with nearly all binaries within a 3-day orbital period residing in a well-defined "synchronization zone". Beyond this, a minority of asynchronous systems persist, which cannot be easily explained by our application of current tidal mechanisms or by differential rotation.

Artificial intelligence systems increasingly operate in decision-critical environments where probabilistic outputs and Human-in-the-Loop (HITL) interactions reshape user engagement. Traditional user experience (UX) frameworks, designed for deterministic systems, fail to capture these evolving sociotechnical dynamics. This paper argues that in AI-enabled HITL systems, UX must transcend frontend usability to encompass backend performance, organizational workflows, and decision making structures. We employ a mixed-methods approach, combining an inductive social construction analysis of 269 stakeholder insights with the deployment of an operational HITL video anomaly detection system. Our findings reveal that stakeholders experience AI through multifaceted themes: risk, governance, and organizational capacity. Experimental results further demonstrate how detection behavior and alert routing directly calibrate human oversight and workload. Grounded in these results, we formalize a new evaluative framework centered on four sociotechnical metrics: Accuracy (FPR/FNR), Operational Latency (response time), Adaptation Time (deployment burden), and Trust (validated automation scales). This framework redefines UX as a multi-layered construct spanning infrastructure and governance, providing a rigorous foundation for evaluating AI systems embedded within complex real-world ecosystems.

The proliferation of artificial intelligence applications on edge devices necessitates efficient transport protocols that leverage multi-homed connectivity across heterogeneous networks. While Multipath TCP enables bandwidth aggregation, its in-kernel congestion control mechanisms lack the programmability and flexibility needed for achieving efficient transmission. Additionally, inherent measurement noise renders network state partially observable, challenging data-driven approaches like deep reinforcement learning (DRL). To address these challenges, we propose a Transformer-based Congestion Control Optimization (TCCO) framework for multipath transport. TCCO employs a decoupled architecture that offloads control decisions to an external decision engine via a lightweight in-kernel client and user-space proxy, enabling edge devices to leverage external computational resources while maintaining TCP/IP compatibility. The Transformer-based DRL agent in the external decision engine uses self-attention to capture temporal dependencies, filter noise, and coordinate control across subflows through a unified policy. Extensive evaluation on both simulated and real dual-band Wi-Fi testbeds demonstrates that TCCO achieves superior adaptability and performance than state-of-the-art baselines, validating the feasibility and effectiveness of TCCO for wireless networks.

With the advent of effective pre-exposure prophylaxis agents, active-controlled HIV prevention trials have become a common study design. Nevertheless, estimating absolute efficacy relative to a placebo remains important. In this paper, we introduce a novel application of proximal causal inference methods to estimate the counterfactual cumulative HIV incidence under placebo for participants in an active-controlled trial of cabotegravir, using external control data from a placebo-controlled trial with similar eligibility criteria. We leverage baseline sexually transmitted infection status and geographic region as negative control outcome and exposure variables, respectively. We address two key challenges: unmeasured differences in HIV risk between trials and statistical difficulties arising from low HIV incidence rates in both studies. To overcome these challenges, we develop two proximal inference approaches: (1) a semiparametric inverse probability of censoring weighting estimator, and (2) a two-stage regression-based strategy tailored to low-event-rate settings. Our theoretical and numerical investigations demonstrate these methods yield reliable estimates of the counterfactual one-year cumulative HIV incidence under placebo, and provide robust evidence of the superior efficacy of cabotegravir compared with placebo. These findings highlight the potential of proximal inference methods to estimate placebo-controlled effects in both single-arm and active-controlled trials by leveraging external controls.

Recent years have seen rapid development in the subject of quantum coding theory, with breakthroughs on many exciting classes of codes, including quantum LDPC codes, quantum locally testable codes, and quantum codes with interesting transversal gates. However, a natural class of quantum codes, which has been well-studied classically, has not yet been treated: those which can be quickly encoded and decoded. This problem concerns the channel capacity setting, where a noise channel sits between perfect encoding and unencoding/decoding operations; this is the setting that is relevant for communication between fault-tolerant quantum computers. In this work, we construct asymptotically good quantum codes that can be encoded and unencoded by quantum circuits of logarithmic depth and consisting of a linear total number of gates. The classical decoding algorithms also run in logarithmic depth and use $\mathcal{O}(n \log n)$ gates, or alternatively a linear number of gates but with higher depth. We further construct explicit and asymptotically good quantum codes whose encoding, unencoding and decoding all use a linear number of gates, and additionally whose encoding and unencoding may be run in logarithmic depth.

Observations of the Cosmic Dawn (CD) and Epoch of Reionization (EoR) are steadily improving, opening new opportunities to study early galaxies through complementary probes. To enable consistent interpretation of these observations, we present Beyond21, a fully open-source Python package that implements flexible prescriptions for Pop II and Pop III star formation and computes the resulting radiation backgrounds and their impact on the intergalactic medium. From this coupled evolution, Beyond21 predicts the global 21-cm signal, UV luminosity functions (UVLFs), the ionization history, and the contribution to the observed cosmic X-ray background (CXB) within a single, self-consistent pipeline. A full global evolution run executes in $\sim0.1 \ {\rm s}$ on a single CPU core, enabling broad, high-resolution parameter exploration. The modular architecture facilitates straightforward modification of astrophysical prescriptions and the incorporation of new physics. As an illustrative example, we implement a scenario in which a small fraction of dark matter is millicharged, leading to baryon cooling through elastic interactions.

Statistics educators recommend teaching with real data with relevant contexts, but defining relevancy is challenging and varies by student. We investigated whether providing student choice of data context increases engagement through a quasi-experiment in two sections of an introductory probability and statistics course at a large public university (n=65 consenting students). Sections alternated as treatment and control: during their treatment, students chose weekly homework from three similar instructor-provided options varying by data context; during control weeks, they received randomly assigned contexts. We found no significant difference in homework grades between treatment and control conditions. However, thematic analysis revealed students with choice reported enhanced engagement and motivation, greater appreciation for statistics' real-world value, and increased autonomy. Students overwhelmingly preferred contexts relevant to their interests, experiences, daily lives, and career paths-though preferences varied considerably across individuals. Based on these findings, we provide four recommendations for statistics educators: (1) use real data with authentic contexts, (2) select contexts students care about, (3) incorporate variety across data contexts, and (4) consider choice as a pedagogical tool.

2D magnetism in triangular lattices has already shown potential for hosting exotic magnetic states. Control of these magnetic states, both in terms of magnetic properties and in terms of charge doping would be the next step. This makes materials which combine triangular lattice magnetic layers with layers hosting interesting structural or electronic properties particularly useful. PrCd$_3$P$_3$, studied in this work, is one of a family of materials where triangular lattice layers of magnetic rare earth ions alternate with semiconducting hexagonal CdP layers. Using Raman scattering spectroscopy we uncover a structural instability in the CdP layers, associated with a soft mode behavior of a phonon in these layers. Raman scattering detects crystal electric field excitations, and confirms a singlet ground state for Pr$^{3+}$ and splitting of the doublet levels as a result of the structural instability in CdP layers. While Pr$^{3+}$ is non-magnetic in PrCd$_3$P$_3$ we speculate that this family of materials can realize control of the magnetic layer through the CdP layer which can become ferroelectric under strain that would relieve frustration.

Interstellar polycyclic aromatic hydrocarbon (PAHs) are an important component of the interstellar medium of galaxies, containing some 10 percent of the elemental carbon. Their vibrational emission dominates the mid-infrared spectra of galactic and extragalactic objects. PAHs control the heating of interstellar neutral gas and the charge balance of molecular clouds. PAHs are formed in the outflows from late type stars through chemical processes akin to those in sooting flames and then further processed in the interstellar medium by UV photolysis and strong shock waves. PAHs are also formed through ion molecule reactions and neutral radical reactions in dense cloud cores. The James Webb Space Telescope has provided a wealth of high-quality spectra that have provided new insights in the characteristics of the interstellar PAH family. Their analysis is supported by dedicated laboratory and quantum chemistry studies, feeding into detailed molecular physics models relevant to astronomical environments. Laboratory studies have also provided deeper insight in the chemical evolution of PAHs in the interstellar medium. This paper will review progress in the field and chart its future.