Let $E\to X$ be a holomorphic vector bundle. We consider a class of a singular Hermitian metrics on $E$ with analytic singularities that contains all Griffiths negative such metrics. One can define, given a smooth reference metric $h_0$, a current $s(E,h,h_0)$ called the associated Segre form, which defines the expected Bott-Chern class and coincides with the usual Segre form of $h$ where it is smooth. We prove that $s(E,h,h_0)$ is the limit of the Segre forms of a sequence of smooth metrics if the metric is smooth outside the degeneracy locus, and in general as a limit of Segre forms of metrics with empty degeneracy locus. One can also define an associated Chern form $c(E,h,h_0)$. We prove that the Lelong numbers of $s(E,h,h_0)$ and $c(E,h,h_0)$ are integers if the singularities are integral, and non-negative for $s(E,h,h_0)$.

We study the unconstrained and the minimax saddle point variants of the convex multi-stage stochastic programming problem, where consecutive decisions are coupled through the objective functions, rather than through the constraints. We approach the problems from the infinite-dimensional policy perspective, but consider an online setting where only the policies corresponding to the actual realization of the underlying stochastic process is needed. This leads to a trackable formulation, where the dimension of the output is linear in the number of stages $T$. We propose hypothetical Mirror Descent Stochastic Approximation (MDSA) for the infinite dimensional policies using stochastic conditional gradients. By taking advantage of the decomposability of the updates across stages and realizations of the underlying stochastic process, we show that the proposed MDSA algorithms admit efficient online implementation, which achieves overall gradient complexity linear in $T$, improving exponentially over all existing algorithms.

In this paper, we show that the one dimensional cubic nonlinear Schrödinger equation is globally well posed in $L^p$ for $2\le p <13/6$. In particular, we prove that the global solution enjoys the persistence property for a twisted variable at any time, which implies the result is a natural exetension of the classical global well-posedness in $L^2$ to $L^p$. The proof exploits the data-decomposition argument originally developed by Vargas-Vega in the functional framework introduced by Zhou.

Divisibility of dynamical maps is a central notion in the study of quantum non-Markovianity, providing a natural framework to characterize memory effects via time-local master equations. In this work, we generalize the notion of divisibility of quantum dynamical maps from the Schrödinger to the Heisenberg picture. While the two pictures are equivalent at the level of physical predictions, we show that the divisibility properties of the corresponding dual maps are, in general, not equivalent. This inequivalence originates from the distinction between left and right generators of time-local master equations, which interchange roles under duality. We demonstrate that Schrödinger and Heisenberg divisibility are distinct concepts by constructing explicit dynamics divisible only in one picture. Furthermore, we introduce a quantifier for the violation of Heisenberg P-divisibility, analogous to the trace-distance-based measure of non-Markovianity, and provide it with an operational interpretation in terms of the guessing probability between effects. Our results show that Heisenberg divisibility is an independent witness of memory effects and highlight the need to consider both pictures when characterizing non-Markovian quantum dynamics.

As emerging technologies continue to shape society, there is a growing emphasis on the need to engage with design ethics as it unfolds in practice to better capture the complexities of ethical considerations embedded in day-to-day work. Positioned within the broader "turn to practice" in HCI, the review characterizes this body of work in terms of its motivations, conceptual frameworks, methodologies, and contributions across a range of design disciplines and academic databases. The findings reveal a shift away from static and abstract ethical frameworks toward an understanding of ethics as an evolving, situated, and inherent aspect of design activities, one that can be cultivated and fostered collaboratively. This review proposes six future directions for establishing common research priorities and fostering the field's growth. While the review promotes cross-disciplinary dialogue, we argue that HCI research, given its cumulative experience with practice-oriented research, is well-equipped to guide this emerging strand of work on design ethics.

Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by clustering the edges of the graph and then assigning a vertex to a community if it has at least one edge in that community, thereby allowing for overlapping clusters of vertices. We apply the idea behind edge clustering to temporal hypergraphs, an extension of a graph where a single edge can contain any number of vertices and each edge has a timestamp. Extending to hypergraphs allows for many different patterns of interaction between edges, and by defining a suitable structural similarity function, our edge clustering algorithm can find clusters of these patterns. We test the algorithm with three structural similarity functions on a large collaboration hypergraph, and find intuitive cluster structures that could prove useful for downstream tasks.

An action of a group on a set is oligomorphic if it has finitely many orbits of $n$-element subsets for all $n$. We prove that for a large class of groups (including all groups of finite virtual cohomological dimension and all countable linear groups), for any oligomorphic action of such a group on an infinite set there exists a finite subset whose stabiliser is not of type $\mathrm{FP}_\infty$. This leads to obstructions on finiteness properties for permutational wreath products and twisted Brin-Thompson groups. We also prove a version for actions on flag complexes, and discuss connections to the Boone-Higman conjecture. In the appendix, we improve on the criterion of Bartholdi-Cornulier-Kochloukova for finiteness properties of wreath products, and the criterion of Kropholler-Martino for finiteness properties of graph-wreath products.

One of the hallmarks of topological systems is the robust quantization of particle transport. It is the origin of the integer-valued quantum Hall conductivity and a potential tool for quantum information technology. Recent experiments on topological pumps constructed by using arrays of photonic waveguides and described by the (lattice-translational invariant) Aubry-André-Harper (AAH) model, have demonstrated both integer and fractional transport of lattice solitons. In these systems, a background medium mediates interactions between photons via a Kerr nonlinearity and leads to the formation of self-bound multi-photon states. Upon increasing the interaction strength a sequence of transitions was observed from a phase with integer transport in a pump cycle through different phases of fractional transport to a phase with no transport. We here present a quantum description of topological pumps of self-bound many-particle states in terms of an effective Hamiltonian of their center-of-mass (COM) motion, which allows to introduce an effective band structure $E_μ(K)$ with $K$ being the COM momentum, and to classify topological phases in terms of generalized symmetries. We provide an explicit analytic expression of the effective Hamiltonian for few particles in the strong interaction limit and present numerical results in the more general case. We identify a topological invariant, an effective single-particle Chern number, which fully governs the soliton transport. Increasing the interaction strength in the AAH model leads to a successive merging of COM bands, which is the origin of the observed sequence of topological phase transitions and also the potential breakdown of topological quantization for some interaction strength.

Classical Graph Signal Processing (GSP) provides a robust framework for analyzing signals on irregular domains, utilizing the graph Fourier transform as a cornerstone for spectral analysis and filtering. However, as data structures grow in complexity, there is an increasing need to handle multi-dimensional information. In this paper, we propose a generalization of the GSP framework by introducing vector-valued graph signals which take values in arbitrary Banach spaces. We define and investigate the fundamental operators of vertex-frequency analysis within this broader setting, including the Fourier transform, convolution, and translation operators. A key contribution of this work is the derivation of operator norm estimates and the establishment of graph-theoretic versions of classical uncertainty principles. We demonstrate how these results depend on the choice of the orthonormal basis and on the underlying $L^p$ norms. By modeling multiple scalar signals as a single vector-valued entity, this framework facilitates the study of inter-signal correlations, providing a flexible and mathematically grounded environment for analyzing multivariate time-series and time-varying signals on complex networks.

Lanthanides are nowadays extensively used to investigate the properties of strongly correlated matter. Nevertheless, exploiting the Zeeman manifold of a lanthanide atom ground state is challenging due to the unavoidable presence of depolarization collisions. Here we demonstrate that in the case of the thulium atom, it is possible to suppress this depolarization by a factor of 1000 with a carefully tuned magnetic field thus opening the way for the efficient use of the Zeeman manifold in quantum simulations.

State-of-the-art solutions detect jamming attacks ex-post, i.e., only when jamming has already disrupted the wireless communication link. In many scenarios, e.g., mobile networks or static deployments distributed over a large geographical area, it is often desired to detect jamming at the early stage, when it affects the communication link enough to be detected but not sufficiently to disrupt it (detection of weak jamming signals). Under such assumptions, devices can enhance situational awareness and promptly apply mitigation, e.g., moving away from the jammed area in mobile scenarios or changing communication frequency in static deployments, before jamming fully disrupts the communication link. Although some contributions recently demonstrated the feasibility of detecting low-power and weak jamming signals, they make simplistic assumptions far from real-world deployments. Given the current state of the art, no evidence exists that detection of weak jamming can be considered with real-world communication technologies. In this paper, we provide and comprehensively analyze new general-purpose strategies for detecting weak jamming signals, compatible by design with one of the most relevant communication technologies used by commercial-off-the-shelf devices, i.e., IEEE 802.11. We describe two operational modes: (i) binary classification via Convolutional Neural Networks and (ii) one-class classification via Sparse Autoencoders. We evaluate and compare the proposed approaches with the current state-of-the-art using data collected through an extensive real-world experimental campaign in three relevant environments. At the same time, we made the dataset available to the public. Our results demonstrate that detecting weak jamming signals is feasible in all considered real-world environments, and we provide an in-depth analysis considering different techniques, scenarios, and mobility patterns.

Cognitive control, the ability to coordinate competing information sources in pursuit of goals, is fundamental to intelligent behavior. We systematically investigate whether Vision Language Models (VLMs) exhibit cognitive control and how computational resources modulate conflict resolution. We construct a benchmark of 4,410 tasks across seven conflict paradigms (Stroop, Flanker, and five realistic variants) spanning multiple difficulty levels and visual complexities, testing 47 VLMs with rigorous experimental control. We find that VLMs exhibit robust congruency effects across all tasks, with larger models systematically resolving conflicts more effectively than smaller models. Critically, VLMs reproduce the fine-grained demand-resource relationship observed in human temporal dynamics: larger models drop below chance on incongruent high-conflict trials while smaller models fail to meaningfully engage and perform at chance, mirroring human behavior at short processing times and establishing parameter count as a proxy for conflict resolution capacity. These findings demonstrate that human-like cognitive control emerges from optimization dynamics in large-scale neural networks, suggesting that adaptive flexibility under conflict may naturally arise through scaling.

Neural operators (NOs) struggle with high-contrast multiscale partial differential equations (PDEs), where fine-scale heterogeneities cause large errors. To address this, we use the Generalized Multiscale Finite Element Method (GMsFEM) that constructs localized spectral basis functions on coarse grids. This approach efficiently captures dominant multiscale features while solving heterogeneous PDEs accurately at reduced computational cost. However, computing these basis functions is computationally expensive. This gap motivates our core idea: to use a NO to learn the subspace itself - rather than individual basis functions - by employing a subspace-informed loss. On standard multiscale benchmarks - namely a linear elliptic diffusion problem and the nonlinear, steady-state Richards equation - our hybrid method cuts solution error by approximately $60\%$ compared with standalone NOs and reduces basis-construction time by about $60$ times relative to classical GMsFEM, while remaining independent of forcing terms and boundary conditions. The result fuses multiscale finite-element robustness with NO speed, yielding a practical solver for heterogeneous PDEs.

The simulation of large-scale classical systems in exponentially small space on quantum computers has gained attention. The prior work demonstrated that a quantum algorithm offers an exponential speedup over any classical algorithm in simulating classical dynamics with long-range interactions. However, many real-world classical systems, such as those arising from partial differential equations, exhibit only local interactions. The question remains whether quantum algorithms can still provide exponential speedup under this condition. In this work, we thoroughly characterize the computational complexity of quantum algorithms for simulating such geometrically local systems. First, we dequantize the quantum algorithm for simulating short-time (polynomial-time) dynamics of such systems. This implies that the problem of simulating this dynamics does not yield any exponential quantum advantage. Second, we show that quantum algorithms for short-time dynamics have the same computational complexity as polynomial-time probabilistic classical computation. Third, we show that the computational complexity of quantum algorithms for long-time (exponential-time) dynamics is captured by exponential-time and polynomial-space quantum computation. This suggests a super-polynomial time advantage when restricting the computation to polynomial-space, or an exponential space advantage otherwise. This work offers new insights into the complexity of classical dynamics governed by partial differential equations, providing a pathway for achieving quantum advantage in practical problems.

Microring resonators enable the enhancement of nonlinear frequency mixing processes, generating output fields at frequencies that widely differ from the inputs, in some cases by more than an octave. The efficiency of such devices depends on effective in- and out-coupling between access waveguides and the microrings at these widely separated frequencies. One successful approach is to separate the coupling task across multiple waveguides, with a cutoff waveguide (a waveguide that does not support guided modes above a certain wavelength) being judiciously used to prevent unwanted excessive overcoupling at low frequencies. Here, we examine how such a cutoff waveguide can still induce parasitic loss in the coupling region of a microring resonator, thereby impacting nonlinear device performance. We verified this parasitic loss channel through both experiment and simulation, showing that a waveguide optimized for 532 nm (visible) and 780 nm (near-infrared), while nominally cut off at 1550 nm, can still introduce significant parasitic loss at telecom wavelengths. This is studied in the context of visible-telecom optical parametric oscillation, where the excess parasitic loss can be strong enough to prevent threshold from being reached. Our finding elucidates a major challenge for wideband integrated nonlinear photonics processes when efficient coupling of widely-separated frequencies is needed.

We report on a first-principles numerical study of magnetic reconnection in plasmas with different initial ion-to-electron temperature ratios. In cases where this ratio is significantly below unity, we observe intense wave activity in the diffusion region, driven by the ion-acoustic instability. Our analysis shows that the dominant macroscopic effect of this instability is to drive substantial ion heating. In contrast to earlier studies reporting significant anomalous resistivity, we find that anomalous contributions due to the ion-acoustic instability are minimal. These results shed light on the dynamical impact of this instability on reconnection processes, offering new insights into the fundamental physics governing collisionless reconnection.

Floods pose significant risks to human lives, infrastructure, and the environment. Timely and accurate flood forecasting plays a pivotal role in mitigating these risks. This study presents a proof-of-concept for a Digital Twin framework aimed at improving flood forecasting in the Alzette Catchment, Luxembourg. The approach integrates satellite-based Earth observations, specifically Sentinel-1 flood probability maps, into a particle filter-based data assimilation (DA) process to enhance flood predictions. By combining the GloFAS global flood monitoring and GloFAS streamflow forecasts products with DA using a high-resolution LISFLOOD-FP hydrodynamic model, the Digital Twin can provide daily flood forecasts for up to 30 days with reduced prediction uncertainties. Using the 2021 flood event as a case study, we evaluate the performance of the Digital Twin in assimilating EO data to refine hydraulic model simulations and issue accurate forecasts. While some limitations, such as uncertainties in GloFAS discharge forecasts, remain large, the approach successfully improves forecast accuracy compared to open-loop simulations. Future developments will focus on constructing more adaptively the hazard catalog, and reducing inherent uncertainties related to GloFAS streamflow forecasts and Sentinel-1 flood maps, to further enhance predictive capability. The framework demonstrates potential for advancing real-time flood forecasting and strengthening flood resilience.

Quantum key distribution relies on quantum mechanics to securely distribute cryptographic keys, offering security but necessitating complex infrastructure and significant resources for practical implementation. Quantum keyless private communication ensures information-theoretic security in free-space communication, with simpler setups, and without the need for secret keys by leveraging the wiretap channel model. Here we propose a variant of quantum keyless private communication using polarization encoding and experimentally validate both the original on-off keying method and the polarization-multiplexed approach using time-multiplexed threshold single-photon detectors as photon counting detectors. Our analysis highlights the advantages of polarization-multiplexed schemes for daylight operation. This work paves the way towards practical and scalable quantum communication systems, with potential applications extending to space-based communication.

This paper introduces persiansort, new stable sorting algorithm inspired by Persian rug. Persiansort does not have the weaknesses of mergesort under scenarios involving nearly sorted and partially sorted data, also utilizing less auxiliary memory than mergesort and take advantage of runs. Initial experimental showed, this method is flexible, powerful and works better than mergesort in almost all types of data. Persiansort offers several advantages over merge methods make it a potential replacement.

We study the scalar curvature $R$ of the vector moduli space of 5d $\mathcal{N}=1$ supergravities, obtained by compactifying M-theory on a Calabi--Yau three-fold. We find that $R$ can only diverge at points where some gauge interactions go to infinite coupling in Planck units and become SCFTs or LSTs decoupled from gravity and other vector multiplets. For 5d SCFTs of rank $r\leq 2$ divergences occur if, additionally, the SCFT still couples to the vevs of such vector multiplets, so that along its Coulomb branch its gauge kinetic matrix and/or string tensions depend on some non-dynamical parameters. If the strong coupling singularity is better understood as a 6d $(1,0)$ SCFT, as in some decompactification limits, then divergences in $R$ arise when the SCFT is endowed with a non-Abelian gauge group.

Payments in parametric insurance solutions are linked to an index and thus decoupled from policyholders' true losses. While this principle has appealing operational benefits compared to traditional indemnity coverage, i.e. is very efficient and cost effective, a downside is the discrepancy between payouts and actual damage, called basis risk. We show that in an asymmetrically weighted mean square error framework, the basis risk-minimizing payment schemes for pure parametric and parametric index insurance contracts can be expressed as conditional expectiles of policyholders' true loss given a compensation-triggering incident. We provide connections to stochastic orderings and demonstrate that regression approaches allow easy implementation in practice. Our results are visualized in parametric coverage for cyber risks and agricultural insurance.

Let $F_n$ be the $n\times n$ Fourier matrix on the cyclic group $\mathbb Z_n$, a renowned theorem of Chebotarëv asserts that all minors in $F_n$ for prime $n$ are non-zero. In this short note it is shown that (i) all principal minors in the Kronecker product $F_p\otimes F_q$ are non-vanishing (principal non-singularity) for distinct odd primes $p,q$ if $q$ is large enough and generates the multiplicative group $\mathbb Z_p^*$; (ii) the Fourier matrix on $\mathbb Z_2^k \times \mathbb Z_q$ is principally non-singular upon permutation (in particular, for $k=1$ the identity permutation suffices) for odd prime $q$ and $k=1,2,3$. The proof is just an exposition of existing techniques reorganized in a unified way. The result will have implications in combining Riesz bases of exponentials.

This work presents a macroscopic model for the flow of two immiscible and incompressible fluids within inhomogeneous porous media. At the pore scale, the flow is governed by the full Navier-Stokes equations while the phase interface evolution is described by the Cahn-Hilliard equation. Applying the volume averaging method, we rigorously derive upscaled equations that characterize the Darcy-scale behavior of the two-phase system. The derivation yields unclosed terms originating from spatial derivations, which are subsequently closed by modeling them as functions of averaged quantities and specific transport coefficients. These coefficients are evaluated by solving localized closure problems defined on representative elementary volumes (REVs). A key contribution of this study is the formal incorporation of wetting behavior into the averaged chemical potential. We further discuss the theoretical distinctions between the proposed framework and standard empirical two-phase Darcy models. Finally, numerical simulations of the upscaled equations are performed, demonstrating the model's capability to capture essential two-phase flow characteristics in porous media.

Instrumental variables are a popular tool to infer causal effects under unobserved confounding, but choosing suitable instruments is challenging in practice. We propose gIVBMA, a Bayesian model averaging procedure that addresses this challenge by averaging across different sets of instrumental variables and covariates in a structural equation model. This allows for data-driven selection of valid and relevant instruments and provides additional robustness against invalid instruments. Our approach extends previous work through a scale-invariant prior structure and accommodates non-Gaussian outcomes and treatments, offering greater flexibility than existing methods. The computational strategy uses conditional Bayes factors to update models separately for the outcome and treatments. We prove that this model selection procedure is consistent. In simulation experiments, gIVBMA outperforms current state-of-the-art methods. We demonstrate its usefulness in two empirical applications: the effects of malaria and institutions on income per capita and the returns to schooling. A software implementation of gIVBMA is available in Julia.

The Casimir effect and superconductivity are foundational quantum phenomena whose interplay is an open question in physics, with significant implications for electron physics, quantum gravity, and high-temperature superconductivity. Determining how Casimir forces behave across a superconducting transition remains elusive due to the difficulty of realizing precise alignment, cryogenic operation, and isolating small force changes from competing effects. Recent theories predict milli-Pascal jumps in Casimir pressure across the transition, motivating experiments capable of reaching well below this regime. Here, we demonstrate an on-chip superconducting nanomechanical platform that overcomes these long-standing challenges, achieving the most parallel Casimir configurations to date. Our microchip-based parallel plates reach unprecedented area-to-separation ratios, exceeding past experiments across superconducting transitions by three orders of magnitude and yielding the strongest Casimir forces generated between compliant surfaces. Scanning tunneling microscopy (STM) directly detects the resonant motion of a suspended nanoscale plate with subatomic precision in lateral positioning and displacement, enabling suppression of van der Waals, electrostatic, and thermal effects. With verified micro-Pascal pressure resolution, our platform provides a credible entry point into a new field of quantum experiments, enabling exploration of Casimir-superconductivity interactions with the stability, parallelism, and sensitivity required to access this regime of physics.

Multi-color visible light communication (VLC) can increase throughput and enable joint lighting and communication operation, but practical color-based schemes such as color shift keying (CSK) typically rely on receiver optical filters whose nonideal passbands and spectral overlap introduce color crosstalk and significant SNR loss. This paper proposes a DC-biased quartered composite transform (QCT) transmission framework for quadrichromatic red, amber, green, blue (RAGB) luminaires that enables filterless multiple streams reception with a single photodiode. The method partitions the information symbols into four parallel real-valued streams and applies a set of mutually orthogonal QCT synthesis matrices designed from the invariances of the matched-filtered circulant channel; at the receiver, matched filtering and QCT-domain projection yield four decoupled scalar subchannels that admit single-tap equalization. A unified evaluation is carried out under common illumination constraints (CCT/CRI and illuminance uniformity) and throughput-matched configurations against RAGB-CSK and conventional DCO-OFDM baselines. In an indoor scenario, QCT attains up to 48.95 dB average effective SNR, providing 15.1-22.7 dB gain over CSK and 15.6-26.4 dB gain over DCO-OFDM, while achieving essentially identical BER to DCO-OFDM in linear AWGN. Under matched mean optical power, QCT also yields near zero clipping distortion and a consistent 0.7-1 dB PAPR reduction relative to DCO-OFDM, supporting power efficient and robust filterless multi-color VLC without sacrificing lighting quality.

Future greenhouse gas neutral energy systems will be dominated by renewable energy technologies providing variable supply subject to uncertain weather conditions. For this setting, we propose an algorithm for capacity expansion planning: We evaluate solutions optimised on a single years' data under different input weather years, and iteratively modify solutions whenever supply gaps are detected. These modifications lead to solutions with sufficient capacities to overcome periods of cold dark lulls and seasonal demand/supply fluctuations. A computational study on a German energy system model for 40 operating years shows that preventing supply gaps, i.e. finding a robust system, increases the total annual cost by 1.6-2.9%. In comparison, non-robust systems display loss of load close to 50% of total demand during some periods. Results underline the importance of assessing the feasibility of energy system models using atypical time-series, combining dark lull and cold period effects.

We derive explicit representations for the (Siegmund) dual and the inverse flow of generalized Ornstein-Uhlenbeck processes whenever these exist. It turns out that the dual and the process corresponding to the inverse stochastic flow are again generalized Ornstein-Uhlenbeck processes. Further, we observe that the stationary distribution of the dual process provides information about the hitting time of zero of the original process.

We propose a descriptor for molecular electronic structure that is based solely on the one- and two-electron integrals but is translationally, rotationally, and unitarily invariant. Then, directly exploiting size consistency, we train and fine tune a neural network to predict the energies of strongly-correlated systems, specifically hydrogen clusters. We use an attention mechanism to formulate a size-independent approach that uses and preserves size-consistency. Therefore, training on few-electron systems can guide predictions for systems with more electrons. Our results are more accurate than alternative geometry-based machine-learning models.

The Eberlein diagonalization method is an iterative Jacobi-type method for solving the eigenvalue problem of a general complex matrix. In this paper we develop the block version of the Eberlein method. We prove the global convergence of our block method and present several numerical examples.