Magnetic fields with field strengths between $10^{-17}\,$G and a few Nanogauss are expected to exist today in the intergalactic medium (IGM). Their origin is unknown, but may be of primordial nature, in which case they would have influenced the thermal and ionization history of the IGM as well as the growth of small-scale matter perturbations. In this work, we revisit constraints on Primordial Magnetic fields (PMFs) by consistently accounting for their energy losses through ambipolar diffusion and decaying turbulences from recombination through the epoch of reionization, which progressively reduces the magnetic field strength over time. We implement these effects in ${\tt HyRec}$ and ${\tt exo21cmFAST}$ to model the interplay between PMFs and astrophysical processes up to reionization. Using a neural-network emulator (${\tt NNERO}$), we perform a MCMC analysis that combines late-time probes of the reionization history and galaxy UV luminosity functions. We find that including PMF energy losses significantly relaxes previous bounds, as the reduced field strength suppresses their imprint on observables. Employing a Fisher matrix analysis, we estimate the sensitivity of the 21$\,$cm signal experiment HERA to the PMFs' imprint on intergalactic medium perturbations and show that 21$\,$cm cosmology could significantly improve on current bounds. Our results highlight the importance of modeling PMF evolution self-consistently with the IGM evolution to extract current bounds and future sensitivities.

We generalize results concerning Gel'fand integration of functions taking values in the space of operators on Hilbert spaces to certain Banach spaces. Building on ideas from \cite{M24} we provide sufficient conditions for the Gel'fand integral to be $(q,p)$-summing and we use the developed techniques to answer a question posed in the mentioned article. Applications to positive operator-valued functions between certain function spaces are also given.

We introduce a class of zero mean curvature surfaces with singularities in the isotropic 3-space, called ZMC-faces. As a main result, we establish three Osserman-type inequalities for a ZMC-face under certain assumptions on both completeness and finiteness of the total curvature. The equality conditions of these inequalities are related to the asymptotic behaviors of the ends. Moreover, we present several examples of ZMC-faces attaining equalities in these inequalities.

Attendance tracking in educational institutions, when conducted through traditional methods, leads to structural problems that consume instruction time and threaten academic integrity. Attendance durations spanning several minutes in primary and secondary education and exceeding ten minutes in higher education, combined with the proxy attendance problem of signing on behalf of someone else, demonstrate the need for electronic systems. Most existing electronic solutions rely on biometric authentication, which raises legal and ethical risks under the European General Data Protection Regulation (GDPR), the Turkish Personal Data Protection Law (KVKK), and the United States Family Educational Rights and Privacy Act (FERPA). Systems using RFID alone provide no built-in safeguard against proxy attendance through card transfer. This study proposes a biometric-free IoT attendance system addressing both deficiencies. The prototype consists of an RFID module, RFID cards, weight sensors, a Bluetooth module, and an Arduino UNO microcontroller. After the student presents their RFID card, the weight sensor measurement is compared against a statistical reference range of 350 individuals (aged 18-22) compiled from three Kaggle datasets; no personal biometric data is recorded. A Python-based GUI performs student management, course tracking, and CSV-based reporting via Bluetooth. Qualitative tests in conditions close to a real classroom have shown that the RFID reading, weight verification, Bluetooth communication, and GUI modules operate in an integrated manner as expected. The proposed system offers a low-cost and reproducible solution that aims to reduce proxy attendance without storing biometric data.

Using molecular dynamics simulations driven by a machine-learned interatomic potential, we investigate at low to intermediate pressures the $λ$-transition of sulfur, a temperature-induced polymerization. At ambient pressure, we capture the melting of crystalline cyclo-octasulfur into a liquid of molecular rings. Within this liquid, the concentration of non-S$_8$ rings increases with temperature; we show that these molecules act as reactive centers, which eventually trigger polymerization. We reproduce key experimental signatures of the $λ$-transition, including the sharp increase in heat capacity and the pronounced dependence of the transition temperature on the heating rate. Building on this, we reconstruct a phase diagram of polymerization up to intermediate pressures. Our results reveal a moderate decrease of the polymerization temperature with pressure, culminating with its merging with the melting line at a critical point. Beyond this point, we provide direct evidence of polymerization emerging from the crystalline phase. By analyzing temperature-ramp trajectories, we observe the formation of non-S$_8$ rings, open chains, and extended polymeric structures which retain features of the crystalline arrangement; further heating the system leads to disorder taking over through melting. Polymerization is therefore initiated slightly before melting. Altogether, our findings provide a microscopic picture of the $λ$-transition throughout the sulfur phase diagram.

The Square Kilometre Array (SKA) is expected to start science operations in 2030 and by that time there could be up to 10$^5$ artificial satellites in Earth's orbit, comprising an increase of an order of magnitude compared to 2024. Most of these new satellites will belong to satellite megaconstellations aimed at providing communication services all over Earth. These satellites create radio frequency interference (RFI) that can impact the observations of modern radio telescopes. In this Letter, we forecast the amount of observing time for which the SKA interferometers will be exposed to satellites, risking RFI contamination. We employed an analytical model and considered two cases of exposure to satellites; (1) satellites that only lie in the main beam and (2) satellites that lie in the main beam or the first sidelobe. We show that for SKA-Low, the exposure is high, with satellites in the beam for 30% of the observation time across half of the frequency range, rising up to 100% below 100 MHz. For SKA-Mid, high frequencies are mostly spared, but observations below 1 GHz could also end up seeing satellites for at least 30% of the time. We conclude that satellites will be unavoidable during SKA observing conditions, risking a strong impact on the RFI environment. This will necessitate a concerted effort to obtain accurate measurements of satellite RFI and to improve our understanding of the impact on various science cases. Finally, new mitigation techniques that are less data-destructive than simple flagging must be introduced.

This study quantifies the association between air pollution and mortality in Ontario, Canada. Exposure-response relationships in air pollution epidemiology are complex due to three features: time-lagged associations, non-linear associations, and multiple pollutants. To address the first two features, two distinct classes of distributed lag non-linear model (DLNM) have been proposed, but extending them to multiple exposures and selecting an appropriate model remain challenging. We propose a unified framework for multiple exposure DLNMs, integrating model specification, estimation, selection and stacking. The framework applies to four different model structures: two additive and two proposed single-index DLNMs, all applicable to general outcome types, including the mortality counts in the motivating application. We develop an estimation approach that applies to all four models. Choosing among the candidate DLNMs is challenging a priori, and we derive an AIC to select among them. As an alternative to selecting a single model, we also extend a model stacking approach to combine inferences across the four DLNMs and propose an implementation scalable to our dataset with 106,346 observations. In the motivating analysis, the four DLNMs yield different estimates, and the proposed stacking approach identifies significant associations between respiratory mortality and a mixture of PM2.5, O3 and NO2.

This paper introduces the Wide-band Asp-Clean (\texttt{WAsp}) algorithm, a novel scale-sensitive image reconstruction method tailored for wide-band imaging applications. This algorithm is particularly beneficial for thermal noise-limited imaging with aperture synthesis telescopes, where joint spatio-frequency modeling of the sky brightness distribution is critical. The \texttt{WAsp} algorithm replaces the use of the MS-Clean algorithm in the MS-MFS algorithm with the {\tt Asp} algorithm \citep{Asp_Clean}, which itself has been improved for both imaging and runtime performance. With the high sensitivity of current and next-generation telescopes, spatio-frequency modeling in a scale-sensitive basis becomes crucial for ensuring that residuals align with the noise model across the frequency band. Although existing wide-band scale-sensitive algorithms have demonstrated superior performance over scale-insensitive counterparts, they often suffer from well-documented deficiencies, leading to significant wide-scale residuals in Stokes-I at low levels and consequently significant relative errors in spectral index maps. The \texttt{WAsp} algorithm addresses these limitations while maintaining computational efficiency. The implementation can be configured to support narrow-band and wide-band scale-sensitive imaging, spectral-cube imaging applications and joint single-dish and interferometer imaging. To demonstrate improved imaging performance, we show comparison with existing algorithms via carefully developed simulations for stress-testing the algorithms. We also present results from its application to real-world wide-band data, underscoring its effectiveness in practical imaging scenarios.

In this work, we establish continuity properties of strongly singular integral operators for extreme values of $p$. Particularly, weighted $L^\infty$-$BMO$ boundedness is obtained, generalizing Miyachi's result to the context of Muckenhoupt weights. As an application, we get an alternative proof of Chanillo's weighted $L^p$ estimates via extrapolation techniques.

Khintchine's theorem on the measure dichotomy for the set of $ψ$-approximable numbers has been generalized to inhomogeneous and higher-dimensional settings. Allen and Ramírez conjectured that the monotonicity condition can be removed in the inhomogeneous $nm=2$ cases. In this paper, we resolve the $(n,m)=(1,2)$ case for $ψ$ satisfying a polynomial decay condition $ψ(q)=O(q^{-δ})$ for some $δ>0.$

Employing high-vacuum electrospray deposition and scanning tunneling microscopy, we investigated how individual poly(3-hexylthiophene) (P3HT) chains navigated on the periodic energy landscape of a reconstructed Au(111) surface. The resulting polymer conformations were governed by the interplay between the periodically corrugated substrate, in particular the depth and regularity of the modulated surface potential, and thermal energy. On a regularly reconstructed surface, annealing at °C provided sufficient energy for chain segments to overcome energy barriers of the corrugated surface potential landscape, allowing monomers along the chain to experience a strong thermodynamic driving force toward the low-energy valleys on the surface. The adsorbed polymers adopted a state where the polymer conformations were replicating the herringbone pattern. By contrast, on an irregularly reconstructed surface, the correspondingly disordered potential landscape yielded a diverse mix of coiled polymer chains performing a two-dimensional random walk and collapsed chains located in troughs of the energy landscape. Intriguingly, annealing at °C forced polymers to form clusters of many chains. Our results establish that thermal energy and substrate topography represent control parameters for altering polymer conformations, providing a mechanistic framework for rationally designing polymer nanostructures at the molecular level.

The May 2024 solar superstorm highlighted the vulnerability of rapidly expanding low Earth orbit (LEO) satellite networks to severe space weather events. To systematically evaluate LEO network resilience, we introduce an open-source tool, CosmicDancePro. It enables a comprehensive analysis of the effects of solar storms in the LEO satellite network. It integrates real-world multimodal datasets, including space weather measurements from several satellites, upper-atmospheric density conditions from data-driven and high-fidelity physics-based models, and LEO satellite trajectory and LEO network measurement traces to quantify orbital decay driven by enhanced atmospheric density and network connectivity degradation. We utilize CosmicDancePro to analyze the Starlink constellation's behavior during two recent major solar storms. First, we identify the specific fleet management strategies Starlink adopts during the May 2024 solar superstorm and how they differ from its regular orbit-correction strategy. Second, we identify the mechanisms driving the previously unexplained 'W'-shaped altitude variation pattern across orbital planes of LEO constellations. Finally, our network-layer analysis quantifies the connectivity degradation during these storms, revealing transient disruptions that include repetitive short-lived outages, reconfiguration latency spikes above 500 ms, up to 60% increase in uplink loss, distorted diurnal latency patterns, and a 10+ Mbps drop in end-user data rates during storm peaks.

We use geometric Landau analysis to determine the singularity structure of four-point, one-cycle negative geometries in $\mathcal{N}=4$ super-Yang-Mills theory, which represent certain contributions to the logarithm of the four-point amplitude or equivalently the normalized quadrangular Wilson loop with a Lagrangian insertion. By analyzing the relevant Landau diagrams recursively, we prove that this quantity has singularities only at $z=-1,0$ and $\infty$ to all loop orders. This represents a first step towards obtaining a non-perturbative resummation for this quantity at next-to-leading order in the expansion over cycles.

Optical wireless communication (OWC) is a promising technology for supporting data intensive services in indoor environments due to its large unregulated spectrum, high spatial reuse, and potential for multigigabit data rates. In particular, vertical cavity surface emitting laser (VCSEL) based systems enable highly directional transmission, allowing efficient spatial separation of users and improved link performance. However, the use of narrow optical beams also makes system performance highly sensitive to user mobility and device orientation, as movement directly affects beam alignment and optical channel gain. Consequently, power allocation strategies that ignore mobility dynamics often provision excess optical power to maintain reliable connectivity, resulting in inefficient energy use. In this work, a power control framework for dynamic indoor OWC networks that explicitly accounts for mobility driven channel variation is developed. It uses a hybrid Gauss Markov and learning based approach that captures both user movement continuity and behaviour driven orientation changes. The mobility states are then used to guide power allocation decisions. Simulation results show that incorporating mobility aware channel prediction enables more accurate power allocation, and improves energy efficiency compared with conventional power control schemes in dynamic indoor environments.

A nonzero neutrino magnetic moment arises already in the minimally extended Standard Model with right-handed massive Dirac neutrinos. The well-known consequence of the neutrino magnetic moment is the phenomenon of neutrino spin oscillations in a magnetic field. It can manifest itself not only as a lack in the flux of active cosmic neutrinos arriving on Earth but also as characteristic features in low-energy neutrino elastic scattering processes. Following our approach developed earlier, in this work we study the influence of arbitrary spin-flavor state of incoming neutrino on low-energy neutrino scattering off different particles in a detector. We demonstrate that superposition of left- and right-handed helicity neutrino states gives rise to an azimuthal asymmetry in the angular distribution of the recoil momenta. We present numerical calculations for elastic neutrino scattering on electrons, protons and 40Ar and 132Xe nuclei, demonstrating the azimuthal-asymmetry effect. Our results indicate the unique potential of directional neutrino detection to observe the neutrino spin oscillations.

Algorithmic systems increasingly function as epistemic infrastructures that govern the conditions of interpretative access and social belief. Yet, mainstream auditing strategies operationalize fairness primarily in predictive terms - error rates, calibration, or group-level parity - leaving epistemic harms under-theorized and under-measured. We propose a quantitative framework for evaluating forms of epistemic injustice in algorithmic environments. First, we introduce a deficit-based template that models epistemic injustices as gaps between ideal and realized conditions across features such as credibility, uptake, and epistemic agency. We map these deficits to concrete stages of algorithmic mediation, showing how epistemic injustice can persist even when standard fairness constraints are satisfied. Drawing on distributive fairness indices, we distinguish two evaluation stances: resource inequality, where indices are applied to distributions of epistemic goods directly, and capability/rights inequity, where indices are applied to output-induced epistemic opportunity. We provide an epistemic translation of canonical indices, illustrating how they diagnose complementary signatures of unfairness - such as exclusionary tails and hierarchical concentration - and support longitudinal auditing under iterative deployment. We also provide a simulation study of a recommender-mediated opinion dynamics setting, showing how the proposed indices capture the evolution of epistemic unfairness under repeated platform interventions. The result is a measurement framework that makes the epistemic dimension of algorithmic harms explicit for system design and evaluation.

Hydrophobic eutectic solvents (HES) are emerging as sustainable alternatives to conventional organic solvents for the extraction of molecular pollutants from water. Yet, their selectivity remains poorly understood, hindering the predictive design of eutectic solvents beyond empirical success. Here, we present a multiscale strategy to rationalize and predict solute partitioning in HES. Focusing on bisphenol A (BPA) in trioctylphosphine oxide (TOPO):menthol as a prototypical system, we combine monophasic and biphasic molecular dynamics with quantum energy decomposition of dominant solvation motifs. Our methodology captures the experimentally measured BPA spontaneous migration and thermodynamic stabilization in the HES phase but also identifies the microscopic origin of selectivity: cooperative hydrogen bonding couples to strong dispersion and polarization in the hydrophobic eutectic microenvironment. The robustness of our workflow paves the way for the predictive in-silico screening and design of HES formulations for green and sustainable applications.

Equivalence class partitioning is a well-established test design technique mandated by safety standards such as ISO~26262 for systematic testing of safety software. In industrial practice, however, its application to legacy undocumented embedded firmware is often hindered by incomplete or outdated functional specifications. This paper proposes a binary-level methodology for inferring output-oriented equivalence classes directly from compiled firmware, without relying on source-level annotations or external documentation. The approach combines control-flow reconstruction and guided symbolic execution to analyze individual functions and group execution paths according to indistinguishable observable behavior, including return values and output parameters. An optional post-processing step produces human-readable representations to support comprehension and documentation. The methodology is evaluated in an industrial automotive context through a practitioner-based study assessing correctness and interpretability. Results indicate strong alignment with expert expectations and a positive perception of readability and usefulness for supporting function understanding and test design. These findings demonstrate the feasibility and practical relevance of binary-level equivalence class inference for systematic testing of legacy undocumented safety-embedded software.

We present 3D kilonova radiative transfer simulations for a series of binary neutron star merger models. The masses of the neutron stars are varied as well as the total mass of the system and two different equations of state were used (SFHO and DD2), producing a range in dynamical ejecta masses and elemental abundance patterns. In this paper, we focus on the bolometric light curves and spectra in the polar direction for comparison with observations of the kilonova AT2017gfo. We calculate line-by-line opacities and include new calibrated lanthanide atomic data. All of the simulated spectra show strong features from Sr II, La III, Gd III and Ce III, which appear to correspond to features identified in AT2017gfo, although the simulated features are generally more blueshifted. The models with the lowest lanthanide fraction in the polar direction also show a Y II feature. Ce III, Ce II, Nd III and Nd II play an important role in shaping the spectral continuum. While the bolometric luminosities in the polar direction vary with the ejecta mass of each model, we find only little sensitivity of the spectral properties to the merger configuration. Our study demonstrates that dynamical ejecta alone can reproduce (although at earlier times) many spectral properties of AT2017gfo, suggesting dynamical ejecta may have a strong impact on the early spectral evolution. However, future simulations are needed to also elucidate the role of other ejecta components for shaping the kilonova spectrum.

We study the inverse optimal transport problem of recovering the ground cost from an optimal transport plan. In discrete settings, this problem reduces to inverse linear programming and is intrinsically ill-posed, exhibiting non-identifiability and flat directions. We show that in the continuous setting, the regularity of the marginals fundamentally alters the structure of the inverse problem. Assuming smooth positive densities for the source and target measures, we characterize the second variation of the optimal transport functional with respect to the ground cost in Hölder spaces. In particular, we show that it is non-degenerate modulo the natural transport invariances, yielding a strict curvature property that is absent in discrete transport. As a consequence, we obtain local identifiability and stability results for inverse optimal transport. For the structured family of bilinear costs (i.e. Mahalanobis parametrizations), the ground cost can be uniquely recovered--up to the intrinsic invariances--from a single optimal coupling under a natural spanning condition. We further show that this identifiability property is generic under arbitrarily small perturbations of the marginals, while settings where the optimal transport map is affine (for instance Gaussian or elliptical marginals) remain degenerate. Finally, we establish precise bounds on the bias and statistical variance of inverse optimal transport under entropic regularization. These results reveal a structural parallel between forward and inverse optimal transport: regularity of the marginals ensures smooth optimal maps in the forward problem, while non-degeneracy of the induced transport plan yields curvature and local invertibility in the inverse problem.

In many problems in optimal control, one seeks to minimise an objective function subject to constraints on the velocity of the system. Imposing these constraints directly -- the ``hard-constrained'' approach -- is often analytically and computationally challenging. A natural alternative is to penalise violations of the constraints, solving a sequence of ``soft-constrained'' problems indexed by a penalty parameter $q$, and hoping that solutions converge to solutions of the hard-constrained problem as $q \to \infty$. We show that this approach is justified when applied to a broad class of geometric control problems on a Riemannian manifold $(M,g)$. We first consider the case where there are no autonomous dynamics, and so the control problem reduces to the problem of finding a curve of minimal length or energy between two points, subject to a nonholonomic velocity constraint/penalty determined by the choice of a bracket-generating subbundle $D$ of $TM$. We show that any sequence of solutions to the soft-constrained problem has an accumulation point which is a solution to the hard-constrained problem. Subsequently, we show how to transform a broad class of optimal control problems to the problem of finding a geodesic, by trivialising the inherent dynamics of the system using a change of coordinates inspired by the interaction picture transformation in quantum mechanics.

We study the sharp bounds of $\mathbb{E}[X_1\cdots X_d]$ when the univariate marginal distributions are known, but the dependence structure between them is unspecified. Maximizing products over non-negative variables is straightforward via the comonotonic coupling, but the problem is more subtle when the marginals can take both positive and negative values. Specifically, two negative realizations can be matched to yield a positive product, whereas a single negative realization necessarily yields a negative product. We propose a decomposition of the problem into a magnitude part and a sign part, and show that universal upper and lower bounds for the product expectation follow from the comonotonic coupling of the absolute values and properly chosen sign vectors. Under a mild regularity assumption, we give necessary and sufficient conditions for these universal bounds to be attainable. For the upper bound, the marginal sign-bias vector must belong to the even-parity polytope, while for the lower, the corresponding condition involves the odd-parity polytope. We construct the extremal couplings via measurable selections on the parity polytope whenever these conditions hold. We study the case of identical marginals in more detail and provide examples of non-symmetric extremal coupling that achieve the universal bounds. We explicitly construct the extremal copulas in three dimensions, and use a recursive parity decomposition to obtain higher-dimensional extremal copulas from the trivariate ones.

Drug delivery to the brain is limited by the blood-brain barrier (BBB). We developed a capacitive micromachined ultrasonic transducer (CMUT)-based transcranial focused ultrasound system capable of both delivering therapy via BBB opening and monitoring microbubble activity across a broad frequency range. The performance of the geometrically focused half-ring array consisting of five transmitters and one receiving element was first assessed through simulations and in-vitro acoustic measurements with microbubbles. Use of phase-inversion (PI) during transmission effectively suppressed CMUT-generated harmonics and enhanced broadband detection of microbubble emissions. In rats, the same system achieved spatially localized BBB opening, confirmed by T1-weighted magnetic resonance imaging. BBB permeability mapping using dynamic contrast-enhanced magnetic resonance imaging (Ktrans) scaled with pressure. Time-resolved acoustic spectra captured microbubble arrival and decay kinetics, and 7-20dB enhancement in the effective dynamic range is observed with PI processing of acoustic emission signals. Together, these findings establish an integrated CMUT platform for combined therapeutic and sensing applications for BBB opening in small animal models, providing a foundation for future real-time, frequency-agile, closed-loop control of ultrasound-mediated drug delivery to the brain.

Dark matter and baryons acquire a relative velocity after decoupling in the early Universe. Baryons are gravitationally unstable only above their Jeans scale, while cold dark matter (CDM) is unstable on all scales. We show for the first time that their relative drift triggers a resonant gravitational instability that drives sound waves in baryons. When the projected DM drift is subsonic, the stable oscillatory branch of baryons resonates with the Doppler-shifted DM mode, producing exponentially growing perturbations whose growth rates exceed the intrinsic CDM growth rate. The instability peaks below the baryon Jeans scale and, in baryon-dominated environments, opens a window of complete stability between the Jeans scale and the resonance. Supersonic drift suppresses growth, as previously noted. The resonant coupling also transfers momentum between the species, creating a non-viscous, collisionless drag. We derive an accurate analytical approximation for the growth rate at resonance and show that the associated timescales range from years to tens of millions of years across diverse environments -- planets, protoplanetary disks, stars, molecular clouds, galaxies, and galaxy clusters -- typically much shorter than their ages. In an expanding FLRW universe, the instability enhances baryon density perturbations at different redshifts for appropriately oriented modes while suppressing the growth of those aligned with the DM stream. The universe thus sings across all scales, and this resonant mechanism provides the means to listen: it offers a novel probe of dark matter through its seismic imprint on astrophysical objects and may explain long-standing puzzles such as the persistence of spiral arms and the heating of the intracluster medium in galaxy clusters.

Near-term hardware is constrained by high error rates, small qubit counts, and relatively low output fidelity, making the execution of large, high performance quantum circuits difficult. Circuit partitioning (or circuit cutting) has emerged as a promising approach to circumvent these limitations by decomposing circuits into smaller subcircuits at two-qubit interaction points. However, it remains unclear which classes of circuits benefit the most from partitioning and under what hardware conditions it is most effective. In this work, we evaluate the suitability of quantum circuits for partitioning from three perspectives: improving fidelity, enabling distributed execution, and scaling to larger circuit sizes. Specifically, we compare uncut circuit execution against two circuit partitioning approaches: Qiskit's automatic cut finding technique and a custom performance optimized circuit cutting method. We also measure these across GHZ, QFT, brickwork, and random quantum circuits ranging from 4 to 14 qubits, using mean absolute error of expectation values and overall output fidelity. Our results show that partitioning benefits larger, highly interconnected circuits, with our custom method reducing error by up to 55\% and improve fidelity for GHZ circuits, but degrading performance for brickwork circuits at larger scales.

Submodular width is a central structural measure governing the complexity of conjunctive query evaluation. In this paper we recast submodular width in geometric terms. We how that submodular width can be approximated, up to a factor $3/2$, by a new branchwidth parameter defined in terms of edge separations in the hypergraph and the costs induced on them by admissible submodular functions. This reformulation turns lower bounds on submodular width into the problem of constructing well-balanced edge separations whose induced cost remains small. We then express this connection through a variational characterisation in terms of a convex body. Using these tools, we relate submodular width to more familiar graph-theoretic notions, including line-graph treewidth and multicommodity flow, and obtain general conditions under which submodular width is tightly linked to generalised hypertree width. In particular, under various natural conditions we show that \[ subw(H) \in Ω\left(\frac{ghw(H)}{\log ghw(H)} \right). \]

Tensor operations dominate modern computational workloads, yet their further acceleration demands hardware platforms with greater parallelism. Although photonic computing provides a compelling route for parallel processing, fully exploiting all native multiplexing dimensions of optical fields is impeded by the challenges in routing and programming light in all dimensions simultaneously. Here we introduce FieldCore, a fully multiplexed photonic tensor core that jointly harnesses wavelength, radio-frequency, guided-mode, time and space dimensions, thereby enabling parallelism to scale multiplicatively within a single optical field. Enabled by inverse-designed silicon photonics, FieldCore preserves a uniform programmed computation across all multiplexed channels in parallel. Experimentally, we validate and benchmark its performance from ultra-high-baudrate arithmetic operations to high-fidelity image convolution and parallel handwritten-digit recognition. We further use FieldCore to unlock applications that naturally require high-dimensional data processing, such as high-dimensional hyperspectral classification and massively parallel mechanical fault diagnosis. Our FieldCore supports an estimated aggregate compute throughput of 69.12 tera operations per second (TOPS) and accommodates up to 1,800 parallel input streams within a single core, establishing a scalable paradigm for fully multiplexed photonic tensor computing and AI inference.

Writing code requires significant time and effort in software development. To automate this process, researchers have made substantial progress using Large Language Models (LLMs) for code generation. Many benchmarks like HumanEval and EvoCodeBench have been created to evaluate LLMs by requiring them to generate code from natural language requirements. However, in enterprise applications and team development, developers typically write code based on structured designs or specifications rather than raw natural language descriptions. This gap between existing benchmarks and real industry development practices means that current benchmark scores may not accurately reflect how much code generation can help automate software development tasks. To address this gap, we propose RealBench, a repository-level code generation benchmark aligned with real-world industry software development practices. Each example includes both natural language requirements and UML diagrams as system design, matching how developers typically receive specifications. Based on the constructed benchmarks, we conduct a systematic evaluation of advanced LLMs' code generation capabilities when provided with structured system designs. The experimental results reveal key insights in current LLMs' capabilities for repo-level code generation aligned with real-world software development practices. First, we notice that regarding repo-level code generation, LLMs show much worse performance and there are significant performance gaps among LLMs. Second, LLMs are good at finding and creating modules defined in UML diagrams, but the quality of generated modules is often poor due to grammar and logic errors. Third, generating the entire repository at once is the best generation strategy on smaller repositories, while generating a complex repository with the module-by-module strategy works better compared to other strategies.

Paper mills are a growing threat to the integrity of science, yet their penetration in conference proceedings remains underexplored despite conferences being more important than journals in some scientific subfields. This study aims to identify papers in conference proceedings whose titles have been offered for sale on social media platforms. We collected more than 4,000 unique publication offers from more than 200 social media channels and used semi-automated methods along with human assessment to match offers with papers published in IEEE conference proceedings. We identified 1,720 papers in 286 IEEE conference proceedings, accounting for up to 23.51% of an individual conference. These problematic papers are co-authored by more than 6,500 researchers from over 3,500 affiliations in 55 countries. The identified papers demonstrate collaboration anomalies, high diversity of affiliations per paper, citation manipulation, a predominance of six-author papers, and content-based irregularities. Our findings show that paper mills are a large, organized, and often public market that commercializes scientific misconduct, not limited to papers, but infiltrating multiple parts of the research ecosystem.

The recent discovery of Ruddlesden--Popper (RP) nickelate thin-film superconductors has opened a new frontier in unconventional superconductivity. Its realization requires both compressive epitaxial strain and highly oxidative growth conditions, yet the microscopic pathway from the parent phase to the superconducting phase remains elusive. Here, X-ray absorption spectra and resonant inelastic X-ray scattering are employed to track this evolution by independently tuning strain and oxygen content in (La,Pr)$_3$Ni$_2$O$_{7-δ}$ thin films. We uncover a remarkable two-step narrative. First, signatures of delocalization emerge in the same way upon two independent tunings: Spectral weight transfers from a ''Upper Hubbard''-like peak to the hole-like peak associated with O $2p_z$ state, and in parallel, the initially localized Ni $3d_{z^2}$ orbital becomes more itinerant followed by the broadening and weakening of $dd$ orbital excitations. Second, as itinerancy increases, long-range spin-density-wave (SDW) order is suppressed in both intensity and correlation length, indicating direct competition with superconductivity. Yet, short-range magnons persist: they become damped but their bandwidth stays unchanged. Our results paint a coherent picture that both strain and oxygenation drive the RP bilayer nickelates towards the superconducting instability, where the O $2p_z$ and Ni $3d_{z^2}$ orbitals become delocalized. Concomitantly, the long-range magnetic order loses coherence and gets suppressed. These findings establish an orbital-selective route to RP nickelate superconductivity, in which the delocalization of the $2p_z$ and $3d_{z^2}$ orbitals and the robust short-range magnons upon the melting of SDW order are prerequisites, providing strong constraints for theory and the roadmap for designing nickelate superconductors.