New general relativity (NGR) is a torsion-based modification of general relativity whose Lagrangian depends on three free parameters, $(c_{a}, c_{v}, c_{t})$. A subset of the parameter space is physically admissible, namely that which simultaneously ensures ghost-freedom, propagation of a spin-2 mode, and a consistent Newtonian limit. In this work we analyze static and spherically symmetric configurations in NGR, both in vacuum and in the presence of a perfect fluid and an electromagnetic field, under the assumption of the existence of a local black-hole horizon. We find that the mere existence of such configurations forces the free parameters into regions associated with known pathological models: theories that either contain ghost instabilities, do not propagate a spin-2 mode, or lack a Newtonian limit. The remaining geometries are regular at the horizon, so the obstruction is not a breakdown of the geometry but a breakdown of the underlying theory. We therefore conclude that, within the class of models examined, NGR does not admit physically meaningful non-trivial black holes distinct from those of the teleparallel equivalent of general relativity.

Superconducting resonators coupled to solid-state qubits offer a scalable architecture for long-range entangling operations and fast, high-fidelity readout. Realizing this requires low photon-loss rates and qubits with tunable electric dipole moments that couple strongly to the resonator's electric field while maintaining long coherence times. For spin qubits, spin-photon coupling is typically achieved via spin-charge hybridization. However, this introduces a fundamental trade-off: a large spin-charge admixture enhances the coupling strength, which boosts readout and resonator-mediated gate speeds, but exposes the qubit to increased decoherence, thereby increasing the threshold required for strong coupling and limiting the time available for accurate state measurement. This makes it essential to identify optimal operating points for each qubit platform. We address this for the donor-based flip-flop qubit, whose microwave-controllable electron-nuclear spin states make it suitable for coupling to microwave resonators. We demonstrate that, by choosing intermediate tunnel couplings that balance strong interaction with long qubit lifetimes, high-fidelity readout and strong coupling are simultaneously achievable. We also map out the respective charge-photon couplings and photon-loss rates required. Furthermore, we show that experimental constraints on charge-photon coupling and photon loss can be mitigated using squeezed input fields. As similar trade-offs appear in quantum-dot-based qubits, our methods and insights extend naturally to these platforms, offering a potential route toward scalable architectures.

High-$z$ quasars are believed to reside in massive dark matter haloes (DMHs), suggesting that they reside in galaxy overdense regions. However, previous observations have shown a range of environments around them. These fields have been limited to luminous quasars ($M_{1450}\lesssim-25$), for which photoevaporation may hinder galaxy formation in their vicinity. Here, we present Subaru/Hyper-Suprime Cam observations of the environments of four low-luminosity quasars ($-24<M_{1450}<-22$) at $z\sim6.18$, which are expected to have a smaller photoevaporation effect. We detect Lyman $α$ emitters (LAEs) with narrowband NB872 imaging, and measure the local LAE overdensity. One quasar (J0844$-$0132) resides in an overdense region ($δ_\mathrm{LAE}=1.97\pm0.40$), whereas the other three fields are consistent with no overdensity. These results hold over the proximity zone of each quasar, suggesting that the diverse environment around quasars is independent of photoevaporation. We find no significant correlation between the LAE overdensities and the characteristics of host galaxies and supermassive black holes. Our quasars have host stellar mass measurements from JWST, allowing us to compare them with the LAE overdensity around galaxies without quasar activity with comparable stellar masses. We find that the LAE overdensity in the J0844$-$0132 field is stronger than that of galaxies with similar stellar mass at $z\sim6$.

Thin-film self-assembly of three-dimensional (3D) microsystems presents a compelling route to integrate complex functionalities into ultra-compact volumes, yet strategies for incorporating tunable ion-conducting elements remain limited. Here, we introduce a strain-induced self assembly platform that transforms lithographically patterned multilayer thin films into functional 3D coaxial Swiss-roll microtubes with total active volumes below 1 uL. A key innovation is the monolithic integration of a chemically tunable polyimide proton-exchange membrane, enabling post-fabrication optimization of ionic transport that balances proton transport with mediator blocking. We further implement a dual-mode operational scheme that decouples microbial metabolism from electrochemical power generation, revealing biofouling, not chemical fouling or membrane degradation, as the dominant failure mechanism in conventional architectures. Critically, optimally treated polyimide membranes exhibit excellent recoverability after fouling, while cell-free mode operation maintains stable performance by physically excluding microorganisms from the microelectronic environment. This integrated bio-electronic microsystem achieves a volumetric power density of ~3.1 mW cm-3 within an ultra-compact footprint of 4.16 mm2. Our work establishes a scalable thin-film engineering approach to create tunable, 3D bioelectronic power sources for autonomous microsystems.

Evidence-informed policy on infections requires estimates of their effects on health. However, pathogenic variation, whereby occurrence of adverse outcomes depends on the infecting strain, might complicate the study of many infectious agents. Here, we consider the interpretation of epidemiologic studies on effects of infections on health when there is heterogeneity in strain-specific effects and information on strain composition is unavailable. We use potential outcomes and causal inference theory for analyses in the presence of multiple versions of treatment to argue that oft-reported quantities in these studies have a causal interpretation that depends on population frequencies of infecting strains. Moreover, as in other contexts where the treatment-variation-irrelevance assumption might be violated, transportability requires additional considerations, beyond those needed for non-compound exposures. This discussion, that considers potential heterogeneity in strain-specific effects, will facilitate interpretation of these studies, and for the reasons mentioned above, also highlights the value of pathogen subtype data.

We present an update to the Hadley Centre Sea-Surface Temperature dataset (HadSST.4.2.0.0) that addresses residual warm bias during the Second World War (WW2). Using an existing quantitative definition of the WW2 warm anomaly we identify Engine Room Intake (ERI) bias corrections as the dominant factor in this warm bias in HadSST4, and use this to propose new constraints on ERI bias estimates prior to 1950. In addition, we implement corrections for truncation bias in observations from the Japanese Kobe Collection, spanning the period from 1933 to 1961. We evaluate the effects of these changes with respect to the previous version of HadSST and compare with the most recent iterations of other SST datasets including ERSSTv6, COBE-SST3 and DCENT. We show that it is possible to remove the WW2 warm anomaly using a physically-based approach that maintains the independence of HadSST from land surface temperature records, and preserves structural diversity within the range of available global SST datasets.

The proper-time method for constructing perturbative dynamical gravitational fields is presented. Using the proper-time method, a perturbative analytical model of gravitational waves against the backdrop of an exact wave solution of Einstein's equations in a Bianchi IV universe is constructed. To construct the perturbative analytical wave model a privileged wave coordinate system and a synchronous time function associated with the proper time of an observer freely moving in a gravitational wave were used. Reduction of the field equations, taking into account compatibility conditions, reduces the mathematical model of gravitational waves to a system of coupled ordinary differential equations for functions of the wave variable. Analytical solutions for the components of the gravitational-wave metric have been found. The stability of the resulting perturbative solutions is proven. The stability of the exact solution for a gravitational wave in the anisotropic Bianchi IV universe is demonstrated.

Estimating cell-to-cell variation (CtCV) and state of health (SoH) for battery modules composed of parallel-connected cells is challenging when only module-level signals are measurable and individual cell behaviors remain unobserved. Although progress has been made in SoH estimation, CtCV estimation remains unresolved in the literature. This paper proposes a unified framework that accurately estimates both CtCV and SoH for modules using only module-level information extracted from incremental capacity analysis (ICA) and differential voltage analysis (DVA). With the proposed framework, CtCV and SoH estimations can be decoupled into two separate tasks, allowing each to be solved with dedicated algorithms without mutual interference and providing greater design flexibility. The framework also exhibits strong versatility in accommodating different CtCV metrics, highlighting its general-purpose nature. Experimental validation on modules with three parallel-connected cells demonstrates that the proposed framework can systematically select optimal module-level features for CtCV and SoH estimations, deliver accurate CtCV and SoH estimates with high confidence and low computational complexity, remain effective across different C-rates, and be suitable for onboard implementation.

Double white dwarf (DWD) binaries are natural outcomes of binary stellar evolution and key sources for future space-based gravitational wave (GW) observatories such as Laser Interferometer Space Antenna (LISA). We investigate how different binary interaction channels shape the physical and orbital properties of DWD systems, focusing on component masses, orbital separations, core compositions, and mass transfer rates. Using the binary population synthesis code COMPAS, we evolve $10^7$ binaries with physically motivated initial distributions of binary parameters. Our simulations reproduce the strong bimodality in the final orbital separations, including a pronounced deficit of systems around $100-500 \rm\,R_\odot$, arising from distinct evolutionary pathways: wide DWDs predominantly originate from stable Roche lobe overflow (RLOF), while close DWDs form through unstable RLOF leading to at least one common envelope (CE) phase. Moreover, we show that the core compositions of WDs provide a powerful tracer of evolutionary history: He-core WDs are strongly concentrated in close systems, whereas CO-core WDs span the full separation range and exhibit a small mass gap in wide binaries. We further identify a correlation between the donor mass transfer rate and the final orbital separation, highlighting the impact of non-conservative mass transfer on the resulting orbital configuration of DWD systems. These results underscore the links among evolutionary channels, chemical composition, and mass transfer rates; thereby provide a unique framework for interpreting Gaia DWD samples and forecasting the joint electromagnetic and GW population accessible to LISA.

The third data release of the Gaia mission (Gaia DR3) has enabled large-scale searches for dormant black hole and neutron star binaries with stellar companions at AU-scale separations. A recent study has proposed thousands of dormant black hole and neutron star binary candidates using summary statistics from Gaia DR3 by simulating and fitting Gaia observables. In this work, we perform broadband spectral energy distribution (SED) fitting from the optical to the infrared for 1,328 candidates, incorporating GALEX ultraviolet photometry to assess the presence of hidden hot companions. We quantify ultraviolet excess by comparing observed near-ultraviolet fluxes with single-star SED predictions and further test whether excesses can be explained by non-degenerate stellar companions for sources exhibiting moderate excess. We additionally examine the Galactic kinematics of the sample to identify systems potentially affected by natal kicks during compact-object formation. By combining the ultraviolet and kinematic diagnostics, we identify 182 sources as the highest-priority candidates for follow-up observations, in which 19 are black hole candidates with fit_companion_mass $\geq$ 3 $M_\odot$.

We have discovered a compact hierarchical triple main-sequence star system, which is cataloged as Gaia DR3 1010268155897156864 or TIC 21502513. Hereafter, we call it ``G1010''. G1010 consists of a primary (the most massive) star and inner binary that orbit each other. The primary star is a $0.85_{-0.03}^{+0.03}\;{\rm M}_\odot$ main-sequence (MS) star, and the inner binary components are $0.63_{-0.02}^{+0.02}$ and $0.61_{-0.02}^{+0.02}\;{\rm M}_\odot$ MS stars. The outer and inner orbital periods are $277.2_{-1.3}^{+1.6}$ and $\sim 18.26$ days, respectively. G1010 is categorized as a single-lined spectroscopic binary, and its orbital solution indicates that G1010 possibly accompanies a massive compact object, such as a neutron star or massive white dwarf. In order to confirm the presence of a massive compact object, we have performed several-times low signal-to-ratio (SNR) and one-time high SNR spectroscopic observations, and determined the outer orbital parameters. Moreover, we have deeply analyzed the high SNR spectroscopic data, and found that G1010 accompanies not a massive compact object, but an inner binary. We have investigated G1010's light curve in Transiting Exoplanet Survey Satellite (TESS), and concluded that the inner binary is actually an eclipsing binary, not included in TESS Eclipsing Binary Stars. We have obtained the inner orbital parameters from the TESS light curve. G1010 is similar to compact hierarchical triple star systems previously discovered by eclipse timing variation analysis. Our discovery has shown that such triple star systems can be discovered by combination of low- and high-SNR spectroscopic observations with the help of Gaia DR3 and the upcoming Gaia DR4/DR5.

In molecular aggregates, multiple delocalized exciton states interact with phonons, making the state-resolved spectroscopic monitoring of dynamics challenging. We propose a protocol that combines photon-entanglement-enhanced narrowband excitation of two-exciton states with time-frequency-filtered two-photon coincidence counting. This approach alleviates bottlenecks associated with probing two-exciton dynamics spread across multiple spectral and temporal windows. We demonstrate that non-classical correlations of entangled photon pairs can be used to prepare narrowband two-exciton population distributions, circumventing relaxation in mediating one-exciton states. The evolution of these population distributions and cascading optical transitions can be monitored using time-frequency-filtered two-photon coincidence counting. Numerical simulations for a light-harvesting aggregate highlight the ability of this protocol to suppress or amplify specific pathways. Combining entangled photonic sources with multidimensional photon correlation spectroscopy allows promising applications in spectroscopy and sensing.

Recent lattice QCD simulations and phenomenological models indicate that the nucleon's gravitational form factor $B_N(t)$ remains remarkably small at finite momentum transfer $t$. While $B_N(0) = 0$ is a known consequence of the equivalence principle, the physical origin of its suppression at finite $t$ has not been fully elucidated. In this work, we demonstrate that the smallness of $B_N(t)$ arises from a fundamental cancellation within the nucleon's wave functions. Using light-front holographic QCD, we show that $B_N(t)$ is governed by an antisymmetric factor in the longitudinal dynamics that leads to the exact vanishing of the form factor in the symmetric limit and significant suppression for realistic nucleon structures. Our results suggest that the smallness of $B_N(t)$ is a signature of the nucleon's dominant S-wave character, providing a formal justification for its frequent omission in practical applications like near-threshold $J/ψ$ production.

Over the past decade, Brazil has experienced a decline in vaccination coverage, reversing decades of public health progress achieved through the National Immunization Program (PNI). Growing evidence points to the widespread circulation of vaccine-related misinformation -- particularly on social media platforms -- as a key factor driving this decline. Among these platforms, Telegram remains the only major platform permitting accessible and ethical data collection, offering insight into public channels where vaccine misinformation circulates extensively. This data paper introduces a curated dataset of about four million Telegram posts collected from 119 prominent Brazilian anti-vaccine channels between 2020 and 2025. The dataset includes message content, metadata, associated media, and classification related to vaccine posts, enabling researchers to examine how false or misleading information spreads, evolves, and influences public sentiment. By providing this resource, our aim is to support the scientific and public health community in developing evidence-based strategies to counter misinformation, promote trust in vaccination, and engage compassionately with individuals and communities affected by false narratives. The dataset and documentation are openly available for non-commercial research, under strict ethical and privacy guidelines at https://doi.org/10.25824/redu/5JIVDT

We propose a novel chaos indicator -- time-reversed Shannon entropy (TRSE) -- that leverages the interplay between time-reversal symmetry breaking and information entropy in curved spacetimes. By quantifying statistical discrepancies between forward and backward temporal evolution of particle orbits, TRSE robustly distinguishes chaotic from regular dynamics in non-integrable systems. In contrast, integrable systems exhibit stable, symmetric probability distributions preserved by conserved quantities such as the Carter constant. We validate the method through high-precision numerical simulations in both Kerr and Schwarzschild-Melvin black hole geometries, evolving trajectories forward and backward in time. Furthermore, we refine our previously introduced particle-pair mutual information (MIPP) and perform comprehensive parameter-space scans, revealing a strong quantitative agreement between MIPP and TRSE. The two indicators emerge as complementary probes of chaos: TRSE captures symmetry breaking in orbital evolution, while MIPP measures statistical correlations. Together, they establish a unified framework for diagnosing chaos in general relativistic systems, paving a new path to understand the fundamental nature of chaos in non-integrable systems.

We prove the (graded) Jordan--Hölder multiplicities of (mixed) tilting sheaves on flag varieties admit a geometric interpretation as the hypercohomology of certain sheaves on Richardson varieties in the Langlands dual flag variety. These sheaves are a motivic variant of geometric extensions, and may be described as a tensor product of parity sheaves on the Schubert and opposite Schubert varieties. We also provide an explicit formula for these multiplicities in terms of $\ell$-Kazhdan--Lusztig polynomials.

Structured Query Language (SQL) has remained the standard query language for databases. SQL is highly optimized for processing structured data laid out in relations. Meanwhile, in the present application development landscape, it is highly desirable to utilize the power of learned models to perform complex tasks. Large language models (LLMs) have been shown to understand and extract information from unstructured textual data. However, SQL as a query language and accompanying relational database systems are either incompatible or inefficient for workloads that require leveraging learned models. This results in complex engineering and multiple data migration operations that move data between the data sources and the model inference platform. In this paper, we present iPDB, a relational system that supports in-database machine learning (ML) and large language model (LLM) inferencing using extended SQL syntax. In iPDB, LLMs and ML calls can function as semantic projects, as predicates to perform semantic selects and semantic joins, or for semantic aggregations in group-by clauses. iPDB has a new relational predict operator along with semantic query optimizations that enable users to write and efficiently execute semantic SQL queries, outperforming other state-of-the-art systems by 2.5x mean speedup, with speedups of up to 30x.

The provision of services by more than one operator over a common network infrastructure, as enabled by 5G network slicing, is analyzed. Two business models to be implemented by a network operator, who owns the network, and a virtual operator, who does not, are proposed. In one business model, named \emph{strategic}, the network operator provides service to its user base and the virtual operator provides service to its user base and pays a per-subscriber fee to the network operator. In the other business model, named \emph{monopolistic}, the network operator provides service to both user bases. The two proposals are analyzed by means of a model that captures both system and economic features. As regards the systems features, the slicing of the network is modeled by means of a Discriminatory Processor Sharing queue. As regards the economic features, the incentives are modeled by means of the user utilities and the operators' revenues; and game theory is used to model the strategic interaction between the users' subscription decision and the operators' pricing decision. In both business models, it is shown that the network operator can be provided with the appropriate economic incentives so that it acquiesces in serving the virtual operator's user base (monopolistic model) and in allowing the virtual operator to provide service over the network operator's infrastructure (strategic model). From the point of view of the users, the strategic model results in a higher subscription rate than the monopolistic model.

We introduce IRMaGiC, an algorithm built based on RedMaGiC desgined to enhance the selection of Luminous Red Galaxies (LRGs) across the redshift range $1 \leq z \leq 2$. We show that this method extends the capabilities of the redMaGiC algorithm by applying it to simulated photometric data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Space Telescope's High Latitude Wide Area Survey (HLWAS). By integrating infrared band coverage from Roman HLWAS with LSST's optical bands, IRMaGiC enables red-sequence calibration at higher redshifts. We demonstrate that IRMaGiC reduces scatter and bias in photometric redshift estimates for LRGs at higher redshift, providing more accurate redshift assessments compared to existing methods. Our findings suggest that incorporating infrared data can considerably improve the selection and redshift estimation of LRGs at higher redshift, offering substantial benefits for future cosmological surveys.

We propose a methodology to detect weak Lorentz-violating (LV) backgrounds through the nonlinear shift photocurrent in noncentrosymmetric crystals. Using a spinful Rice--Mele model, we show that a LV background induces a momentum-odd correction to the Bloch Hamiltonian that reshapes the phase of the interband dipole matrix elements. As a result, the shift conductivity develops a robust $π$-periodic modulation as a function of the angle of a perpendicularly applied static electric field, in contrast to a weakly $2π$-periodic response of the Lorentz-symmetric case. This change in angular periodicity provides a signature of LV effects which can be directly identified through a photocurrent measurement. For realistic optical intensities, the predicted signal lies in the picoampere range, which can be enhanced in a matrix of weakly interacting chains, allowing sensitivity to LV coupling strengths of the order of $ξ\sim10^{-24}\,\mathrm{C\,m}$. These results establish nonlinear optical transport as a viable probe of emergent LV effects in solid-state systems.

We propose an active-learning method for nonlinear minimax regression. Given a nonlinear function that can be arbitrarily evaluated over a compact set, we fit a surrogate model, such as a feedforward neural network, by minimizing the maximum absolute approximation error. To handle the nonsmoothness of this worst-case loss, we introduce a smooth $L_\infty$ approximation that enables efficient gradient-based training. The training set is iteratively enriched by querying points of largest error via global optimization. We also derive constant and input-dependent worst-case error bounds over the entire input domain. The approach is validated on approximations of nonlinear functions and nonconvex sets, uncertain models of nonlinear dynamics, and explicit model predictive control laws. A Python library is available at https://github.com/bemporad/maxfit.

Let $n,k$ be positive integers such that $n\geq k$, and let $H$ be a hypergeometric random variable counting the number of black marbles in a sample without replacement of size $k$ from an urn that contains $i\in \{1,\ldots, n\}$ black and $n - i$ white marbles. It is shown that \[ \mathbb{P}(H \ge \mathbb{E}(H)) \ge k/n\, , \, \text{when} \,\, n\ge 8k \, . \] Furthermore, provided that $1\le \mathbb{E}(H)\le \min\{i,k\}-1$ as well as that $\frac{(n-i)(n-k)}{n}>1$, it is shown that \[ \mathbb{P}(H\ge \mathbb{E}(H)) \,\ge\, \frac{e^{-1/12}}{4\sqrt{2}} \cdot \sqrt{\frac{n-1}{n}} \cdot\frac{ \sqrt{\text{Var}(H)} }{1 + \sqrt{1+ \frac{n-1}{n-k}\cdot\text{Var}(H)}}\, . \] Auxiliary results which may be of independent interest include an upper bound on the tail conditional expectation and a lower bound on the mean absolute deviation of the hypergeometric distribution.

A central aspect of every pulsed radar signal processor is the targets Range-Doppler estimation within a Coherent Processing Interval. Conventional methods typically rely on simplifying assumptions, such as linear target motion, narrowband operation, or constant velocity, to enable fast computation. However, these assumptions break down in scenarios involving quadratic range-time behavior, high radial velocities or accelerations, or wideband signals, leading to undesired effects such as intra-pulse Doppler shift/stretch and target migration across Range-Doppler cells. This paper presents a generalized waveform-independent Range-Doppler compression approach that compensates for these effects while maintaining minimal Signal-to-Noise-Ratio loss and practical computational efficiency. The performance limits of the proposed method are analyzed and expressed through a unified metric that depends on both scene and system parameters. Comparison with other approaches is presented, showing their estimation bias and performance degradation.

The process of creating goods and services, measured by their value, is considered a process of creating complexity. This allows us to consider the production system as an open thermodynamic system, and to develop a simple heuristic model of the production process. The model includes three production factors: the index of complexity of production equipment (physical capital $K$), human activity (labour $L$), and the substitutive capacity of equipment (substitutive work $P$). The latter is a contribution to the theory of production from the thermodynamic approach, which also involves introducing technological characteristics of production equipment, such as labour requirement ($\overlineλ$) and energy requirement ($\overline{\varepsilon}$), which indicate the amounts of labour and energy required to operate production equipment. By applying thermodynamic principles, we can understand how labour can be replaced by capital and derive the production function with four different formulations. Two of them are known and used by researchers for interpretation the production phenomena; the thermodynamic approach provides some foundation for economic theory, allowing us to decompose unambiguously the growth rate of output over technological level and the growth rates of production factors. Introducing substitute work as a factor of production and technological characteristics of capital expands our ability to plan and analyse production processes.

With the advancement of vision-language models, web automation has made significant progress. However, deploying autonomous agents in real-world settings remains challenging, primarily due to site heterogeneity, where generalist models lack domain-specific priors for diverse interfaces, and long-horizon instability, characterized by the accumulation of decision drift over extended interactions. To address these challenges, we introduce ColorBrowserAgent (Complex Long-Horizon Browser Agent), a knowledge-evolving agent for robust web automation. Our approach addresses these challenges through two synergistic mechanisms: human-in-the-loop knowledge adaptation that transforms sparse human feedback into reusable domain knowledge, and knowledge-aligned progressive summarization that stabilizes long interactions through memory compression. Extensive experiments on WebArena, WebChoreArena and industrial deployment show that ColorBrowserAgent consistently outperforms strong baselines. It achieves a state-of-the-art success rate of 71.2% on WebArena and maintains 47.4% performance under zero-shot transfer setting on WebChoreArena. In commercial deployment, it improves user satisfaction by 19.3% relatively, verifying its robustness in real-world scenarios.

Generative AI (GenAI) is a powerful technology poised to reshape Trust & Safety. While misuse by attackers is a growing concern, its defensive capacity remains underexplored. This paper examines these effects through a qualitative study with 43 Trust & Safety experts across five domains: child safety, election integrity, hate and harassment, scams, and violent extremism. Our findings characterize a landscape in which GenAI empowers both attackers and defenders. GenAI dramatically increases the scale and speed of attacks, lowering the barrier to entry for creating harmful content, including sophisticated propaganda and deepfakes. Conversely, defenders envision leveraging GenAI to detect and mitigate harmful content at scale, conduct investigations, deploy persuasive counternarratives, improve moderator wellbeing, and offer user support. This work provides a strategic framework for understanding GenAI's impact on Trust & Safety and charts a path for its responsible use in creating safer online environments.

We investigate thermalization and the quantum-classical correspondence in the collective Bose-Hubbard model, focusing on the four-site case. Our analysis of the classical phase-space structure and its excited-state quantum phase transitions leads us to three dynamical regimes: symmetry-breaking low-energy states, an intermediate region where quantum and classical equilibrium states markedly disagree, and a high-energy regime with restored correspondence. The observed classical intermittency above the first excited-state quantum phase transition contrasts with quantum dynamics, which remains trapped in symmetry-breaking sectors despite the existence of a classically connected phase. This mismatch originates from the population of imbalance-carrying eigenstates and persists even for relatively large number of particles. Our results reveal unexpectedly slow convergence to the classical limit, signaling robust finite-size effects in collective many-body dynamics.

Recent diffusion-based image editing methods commonly rely on text or high-level instructions to guide the generation process, offering intuitive but coarse control. In contrast, we focus on explicit, prompt-free editing, where the user directly specifies the modification by cropping and pasting an object or sub-object into a chosen location within an image. This operation affords precise spatial and visual control, yet it introduces a fundamental challenge: preserving the identity of the pasted object while harmonizing it with its new context. We observe that attention maps in diffusion-based editing models inherently govern whether image regions are preserved or adapted for coherence. Building on this insight, we introduce LooseRoPE, a saliency-guided modulation of rotational positional encoding (RoPE) that loosens the positional constraints to continuously control the attention field of view. By relaxing RoPE in this manner, our method smoothly steers the model's focus between faithful preservation of the input image and coherent harmonization of the inserted object, enabling a balanced trade-off between identity retention and contextual blending. Our approach provides a flexible and intuitive framework for image editing, achieving seamless compositional results without textual descriptions or complex user input.

K-ion intercalation in Prussian blue analogs (PBAs) is a well-established charge storage mechanism in potassium-ion batteries; here, we demonstrate that this same ion intercalation process is the basis for nanoscale resistive switching behavior in PBA-base memristive devices. Using C-AFM, we directly visualize and electrically control this intercalation process within sub-100 nm volumes, revealing reversible, localized conductance modulation driven by K-ion intercalation and Fe^(2+)/Fe^(3+) redox reconfiguration. This nanoscale operability highlights the exceptional potential of PBAs for high-scalable and low-dimension memristor-based devices integration. Due to their modular composition, PBAs constitute a chemically rich, earth-abundant materials platform whose electronic and ionic properties can be precisely tuned for specific device functions. K-ion intercalation PBA-based memristor devices, with their single-step, aqueous, and room-temperature fabrication, enable low-cost, large-scale processing compatible with CMOS, without any additional post-fabrication processing. Our findings establish PBAs as a new class of intercalation memristors with scalable nanoscale switching and exceptional materials versatility, toward highly integrated neuromorphic and non-volatile memory technologies. This work provides the first demonstration of intercalation-driven resistive switching under ultrafast voltage sweeps, with PW operating up to 200 V/s and PB up to 50 V/s. This unprecedented speed establishes PBAs as a distinct, high-rate class of K-ion intercalation memristors suitable for fast, high-density neuromorphic and memory applications.

Extreme events such as earthquakes pose significant threats to integrated electricity-gas distribution systems (IEGDS) by causing widespread damage. Existing restoration approaches typically assume full awareness of damage, which may not be true if monitoring and communication infrastructures are impaired. In such circumstances, field inspection is necessary. This paper presents a novel adaptive restoration framework for IEGDS, considering dynamic damage assessment and repair. The restoration problem is formulated as a partially observable Markov decision process (POMDP), capturing the gradually revealed contingency and the evolving impact of field crew actions. To address the computational challenges of POMDPs in real-time applications, an advanced belief tree search (BTS) algorithm is introduced. This algorithm enables crew members to continuously update their actions based on evolving belief states, leveraging comprehensive simulations to evaluate potential future trajectories and identify optimal inspection and repair strategies. Based on the BTS algorithm, a unified real-time decision-making framework is developed for IEGDS restoration. Case studies on two distinct IEGDS systems demonstrate the effectiveness and scalability of the proposed method. The results indicate that the proposed approach achieves an outage cost comparable to the ideal solution, and reduces the total outage cost by more than 15% compared to strategies based on stochastic programming and heuristic methods.