AI-based climate and weather models have rapidly gained popularity, providing faster forecasts with skill that can match or even surpass that of traditional dynamical models. Despite this success, these models face a key challenge: predicting future climates while being trained only with historical data. In this study, we investigate this issue by analyzing boreal winter land temperature biases in AI weather and climate models. We examine two weather models, FourCastNet V2 Small (FourCastNet) and Pangu Weather (Pangu), evaluating their predictions for 2020-2025 and Ai2 Climate Emulator version 2 (ACE2) for 1996-2010. These time periods lie outside of the respective models' training sets and are significantly more recent than the bulk of their training data, allowing us to assess how well the models generalize to new, i.e. more modern, conditions. We find that all three models produce cold-biased mean temperatures, resembling climates from 15-20 years earlier than the period they are predicting. In some regions, like the Eastern U.S., the predictions resemble climates from as much as 20-30 years earlier. Further analysis shows that FourCastNet's and Pangu's cold bias is strongest in the hottest predicted temperatures, indicating limited training exposure to modern extreme heat events. In contrast, ACE2's bias is more evenly distributed but largest in regions, seasons, and parts of the temperature distribution where climate change has been most pronounced. These findings underscore the challenge of training AI models exclusively on historical data and highlight the need to account for such biases when applying them to future climate prediction.

For premium consumer products, pricing strategy is not about a single number, but about understanding the perceived monetary value of the features that justify a higher cost. This paper proposes a robust methodology to deconstruct a product's price into the tangible value of its constituent parts. We employ Bayesian Hierarchical Conjoint Analysis, a sophisticated statistical technique, to solve this high-stakes business problem using the Apple iPhone as a universally recognizable case study. We first simulate a realistic choice based conjoint survey where consumers choose between different hypothetical iPhone configurations. We then develop a Bayesian Hierarchical Logit Model to infer consumer preferences from this choice data. The core innovation of our model is its ability to directly estimate the Willingness-to-Pay (WTP) in dollars for specific feature upgrades, such as a "Pro" camera system or increased storage. Our results demonstrate that the model successfully recovers the true, underlying feature valuations from noisy data, providing not just a point estimate but a full posterior probability distribution for the dollar value of each feature. This work provides a powerful, practical framework for data-driven product design and pricing strategy, enabling businesses to make more intelligent decisions about which features to build and how to price them.

The lack of high-quality public cyber incident data limits empirical research and predictive modeling for cyber risk assessment. This challenge persists due to the reluctance of companies to disclose incidents that could damage their reputation or investor confidence. Therefore, from an actuarial perspective, potential resolutions conclude two aspects: the enhancement of existing cyber incident datasets and the implementation of advanced modeling techniques to optimize the use of the available data. A review of existing data-driven methods highlights a significant lack of entity-specific organizational features in publicly available datasets. To address this gap, we propose a novel InsurTech framework that enriches cyber incident data with entity-specific attributes. We develop various machine learning (ML) models: a multilabel classification model to predict the occurrence of cyber incident types (e.g., Privacy Violation, Data Breach, Fraud and Extortion, IT Error, and Others) and a multioutput regression model to estimate their annual frequencies. While classifier and regressor chains are implemented to explore dependencies among cyber incident types as well, no significant correlations are observed in our datasets. Besides, we apply multiple interpretable ML techniques to identify and cross-validate potential risk factors developed by InsurTech across ML models. We find that InsurTech empowered features enhance prediction occurrence and frequency estimation robustness compared to only using conventional risk factors. The framework generates transparent, entity-specific cyber risk profiles, supporting customized underwriting and proactive cyber risk mitigation. It provides insurers and organizations with data-driven insights to support decision-making and compliance planning.

This study presents a surrogate model designed to predict the Nusselt number distribution in an enclosed impinging jet arrays, where each jet function independently and where jets can be transformed from inlets to outlets, leading to a vast number of possible flow arrangements. While computational fluid dynamics (CFD) simulations can model heat transfer with high fidelity, their cost prohibits real-time application such as model-based temperature control. To address this, we generate a CNN-based surrogate model that can predict the Nusselt distribution in real time. We train it with data from implicit large eddy computational fluid dynamics simulations (Re < 2,000). We train two distinct models, one for a five by one array of jets (83 simulations) and one for a three by three array of jets (100 simulations). We introduce a method to extrapolate predictions to higher Reynolds numbers (Re < 10,000) using a correlation-based scaling. The surrogate models achieve high accuracy, with a normalized mean average error below 2% on validation data for the five by one surrogate model and 0.6% for the three by three surrogate model. Experimental validation confirms the model's predictive capabilities. This work provides a foundation for model-based control strategies in advanced thermal management applications.

This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained Internet of Things (IoT) and edge devices. Thanks to HW-NAS, a 1D convolutional neural network (CNN) is tailored on the ISCX VPN-nonVPN dataset to meet strict memory and computational limits while achieving robust performance. The optimized model attains 96.60% accuracy with just 88.26K parameters, 10.08M FLOPs, and a maximum tensor size of 20.12K. Compared to state-of-the-art models, it achieves reductions of up to 444-fold, 312-fold, and 15-fold in these metrics, respectively, minimizing memory footprint and runtime requirements. The model also achieves up to 99.86% across multiple VPN and traffic classification (TC) tasks; it further generalizes to external benchmarks with up to 99.98% accuracy on USTC-TFC and QUIC NetFlow. In addition, an in-depth study of header-level preprocessing confirms that the optimized model can provide performance across a wide range of configurations, even in scenarios with stricter privacy considerations. Likewise, a reduction in the length of sessions of up to 75% yields significant improvements in efficiency, while maintaining high accuracy with only a negligible drop of 1-2%. However, the importance of careful preprocessing and session length selection in the classification of raw traffic data is still present, as improper settings or aggressive reductions can cause a 7% reduction in accuracy. The quantized architecture was deployed on STM32 microcontrollers and evaluated across input sizes; results confirm that the efficiency gains from shorter sessions translate to practical, low-latency embedded inference. These findings demonstrate the method's practicality for encrypted traffic analysis in constrained IoT networks.

We investigate tasks that can be accomplished with unlabeled graphs, which are graphs with nodes that do not have persistent or semantically meaningful labels attached. New visualization techniques to represent unlabeled graphs have been proposed, but more understanding of unlabeled graph tasks is required before these techniques can be adequately evaluated. Some network visualization tasks apply to both labeled and unlabeled graphs, but many do not translate between these contexts. We propose a data abstraction model that distinguishes the Unlabeled context from the increasingly semantically rich Labeled, Attributed, and Augmented contexts. We filter tasks collected and gleaned from the literature according to our data abstraction and analyze the surfaced tasks, leading to a taxonomy of abstract tasks for unlabeled graphs. Our task taxonomy is organized according to the Target data under consideration, the Action intended by the user, and the Scope of the data at play. We show the descriptive power of this task abstraction by connecting to concrete examples from previous frameworks, and connecting these abstractions to real-world problems. To showcase the evaluative power of the taxonomy, we perform a preliminary assessment across 6 different network visualization idioms for each task. For each combination of task and visual encoding, we consider the effort required from viewers, the likelihood of task success, and how both factors vary between small-scale and large-scale graphs. Supplemental materials are available at osf.io/e23mr.

We study equilibrium finding in polymatrix games under differential privacy constraints. Prior work in this area fails to achieve both high-accuracy equilibria and a low privacy budget. To better understand the fundamental limitations of differential privacy in games, we show hardness results establishing that no algorithm can simultaneously obtain high accuracy and a vanishing privacy budget as the number of players tends to infinity. This impossibility holds in two regimes: (i) We seek to establish equilibrium approximation guarantees in terms of Euclidean \emph{distance} to the equilibrium set, and (ii) The adversary has access to all communication channels. We then consider the more realistic setting in which the adversary can access only a bounded number of channels and propose a new distributed algorithm that: recovers strategies with simultaneously vanishing \emph{Nash gap} (in expected utility, also referred to as \emph{exploitability}) and \emph{privacy budget} as the number of players increases. Our approach leverages structural properties of polymatrix games. To our knowledge, this is the first paper that can achieve this in equilibrium computation. Finally, we also provide numerical results to justify our algorithm.

Index recommendation is crucial for optimizing database performance. However, existing heuristic- and learning-based methods often rely on inefficient exhaustive search and estimated costs, leading to low efficiency (due to the vast search space) and unsatisfactory actual latency (due to inaccurate estimations). Inspired by the refinement strategies of experienced DBAs-who efficiently identify and iteratively refine indexes with database feedback-we present LLMIA, an out-of-the-box, tuning-free index advisor leveraging large language models (LLMs) through in-context learning for index recommendation. LLMIA injects database expertise into the LLM using a high-quality demonstration pool and comprehensive workload feature extraction, while iteratively incorporating database feedback to guide the index refinement. This design enables LLMIA to emulate the decision-making process of expert DBAs: efficiently recommending and refining indexes for various workloads within just a few interactions with the DBMS. We validate LLMIA with extensive experiments on five standard OLAP benchmarks (TPC-H with different scales, JOB, TPC-DS, SSB), where it consistently outperforms or matches 12 baselines by producing superior index recommendations with minimal database interactions. Additionally, LLMIA demonstrates robust generalization on two real-world commercial workloads, delivering high-quality recommendations without the need for additional adaptation or retraining, highlighting its out-of-the-box capability.

As large language models (LLMs) become increasingly embedded in civic, educational, and political information environments, concerns about their potential political bias have grown. Prior research often evaluates such bias through simulated personas or predefined ideological typologies, which may introduce artificial framing effects or overlook how models behave in general use scenarios. This study adopts a persona-free, topic-specific approach to evaluate political behavior in LLMs, reflecting how users typically interact with these systems-without ideological role-play or conditioning. We introduce a two-dimensional framework: one axis captures partisan orientation on highly polarized topics (e.g., abortion, immigration), and the other assesses sociopolitical engagement on less polarized issues (e.g., climate change, foreign policy). Using survey-style prompts drawn from the ANES and Pew Research Center, we analyze responses from 43 LLMs developed in the U.S., Europe, China, and the Middle East. We propose an entropy-weighted bias score to quantify both the direction and consistency of partisan alignment, and identify four behavioral clusters through engagement profiles. Findings show most models lean center-left or left ideologically and vary in their nonpartisan engagement patterns. Model scale and openness are not strong predictors of behavior, suggesting that alignment strategy and institutional context play a more decisive role in shaping political expression.

Histopathologic analysis of stained tissue remains central to biomedical research and clinical care. Virtual staining (VS) offers a promising alternative, with potential to reduce costs and streamline workflows, yet hallucinations pose serious risks to clinical reliability. Here, we formalize the problem of hallucination detection in VS and propose a scalable post-hoc method: Neural Hallucination Precursor (NHP), which leverages the generator's latent space to preemptively flag hallucinations. Extensive experiments across diverse VS tasks show NHP is both effective and robust. Critically, we also find that models with fewer hallucinations do not necessarily offer better detectability, exposing a gap in current VS evaluation and underscoring the need for hallucination detection benchmarks.

As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-making. Participants interacted freely with either a biased liberal, biased conservative, or unbiased control model while completing these tasks. We found that participants exposed to partisan biased models were significantly more likely to adopt opinions and make decisions which matched the LLM's bias. Even more surprising, this influence was seen when the model bias and personal political partisanship of the participant were opposite. However, we also discovered that prior knowledge of AI was weakly correlated with a reduction of the impact of the bias, highlighting the possible importance of AI education for robust mitigation of bias effects. Our findings not only highlight the critical effects of interacting with biased LLMs and its ability to impact public discourse and political conduct, but also highlights potential techniques for mitigating these risks in the future.

To make nature mathematically tractable, the scientific model of the world omits qualia--colors, sounds, tastes, sensations--leaving only what admits of numerical characterization. The "hard problem" of consciousness--the enigma of why and how physical processing gives rise to felt experience--remains unsolved. The Quantification Horizon Theory of Consciousness (QHT) proposes that this enigma reflects a structural limitation of mathematical description: quantitative models capture only quantifiable features of reality; qualia are left out. Yet despite this limitation, QHT argues that such models can account for the unquantifiable--not by explaining it, but by registering its presence, in the form of a signpost. There are specific features of information geometry--compression singularities--that intuitively correspond to the hallmark properties of consciousness and could serve as precisely such signposts. QHT proposes that these singularities mark a quantification horizon--a boundary beyond which quantitative description cannot reach. On this proposal, qualia lie beyond the horizon. From this basis, the theory derives ineffability, privacy, and subjectivity as structural consequences and proposes structural accounts of unity and causal efficacy. The theory proposes substrate-independent dynamical criteria for determining which systems are plausible candidates for consciousness, avoids panpsychism, makes testable predictions, and offers concrete implications for artificial intelligence and artificial consciousness.

We present a comprehensive chemical and age orbital cartography of the Galactic high-$α$ disc using subgiant stars with precise ages, element abundances, and full phase-space information from the \textsl{LAMOST--Gaia} data set. Specifically, we map how average [Fe/H], [$α$/Fe], and age vary across present-day kinematic and orbital coordinates. We analyse the data in full and across mono-abundance populations to measure element abundance-orbital and age-orbital gradients across orbital actions and angular-momenta. Our results show that the high-$α$ disc exhibits clear and coherent gradients in [Fe/H], [$α$/Fe], and age with orbits; these gradients are much stronger and sharper in orbital space than in present-day kinematics, showing that orbital diagnostics recover the intrinsic disc structure of old disc populations more effectively than instantaneous kinematic coordinates. We find that older high-$α$ populations display qualitatively similar element abundance--orbital and age--orbital trends to stars in the low-$α$ disc, although the high-$α$ gradients are generally shallower. The presence of these ordered correlations indicates that the old high-$α$ disc is structured, and preserved a strong fossil record of its early assembly despite the Milky Way's subsequent accretion history. This result implies that later mergers did not fully erase the chemical-orbital and age-orbital structure imprinted during the high-$α$ disc's earliest formation epoch. All together, these findings indicate that the Galactic high-$α$ disc formed mainly through inside-out and upside-down growth.

Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil domains. Although multi-UAV systems enhance connectivity for IoT networks more than single-UAV systems, energy-efficient communication systems and the integration of energy harvesting (EH) are crucial for their widespread adoption and effectiveness. In this regard, this paper proposes a hierarchical ad hoc UAV network with non-linear EH and non-orthogonal multiple access (NOMA) to enhance both energy and cost efficiency. The proposed system consists of two UAV layers: a cluster head UAV (CHU), which acts as the source, and cluster member UAVs (CMUs), which serve as relays and are capable of harvesting energy from a terrestrial power beacon. For the considered IoT network architecture, the outage probability expressions of ground Internet of things (IoT) devices, each CMU, and the overall outage probability of the proposed system are derived over Nakagami-m fading channels with practical constraints such as hardware impairments and non-linear EH. We compare the proposed system against a non EH system, and our findings indicate that the proposed system outperforms the benchmark in terms of outage probability.

As AI systems increasingly permeate high-stakes decision-making, the terminology regarding human involvement - Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human Oversight - has become vexingly ambiguous. This ambiguity complicates interdisciplinary collaboration between computer science, law, philosophy, psychology, and sociology and can lead to regulatory uncertainty. We propose a clarification grounded in causal structure, focused on human involvement during the runtime of AI systems. The distinction between HITL and HOTL, we argue, is not primarily spatial but causal: HITL is constitutive (a human contribution is necessary for the decision output), while HOTL is corrective (external to the primary causal chain, capable of preventing or modifying outputs). Within HOTL, we distinguish three temporal modes - synchronous, asynchronous, and anticipatory - situated within a nested model of provider and deployer runtime that clarifies their different capacities for intervention. A second, orthogonal dimension captures cognitive integration: whether human and machine operate as complementary or hybrid intelligence, yielding four structurally distinct configurations. Finally, we distinguish these descriptive categories from the normative requirements they serve: statutory "Human Oversight" is a specific normative mode of HOTL that demands not merely a corrective causal position, but genuine preparedness and capacity for effective intervention. Because the same person may occupy both HITL and HOTL roles simultaneously, we argue that this role duality must be treated as a design problem requiring architectural and epistemic mitigation rather than mere acknowledgment.

To estimate the causal effect of an intervention, researchers need to identify a control group that represents what might have happened to the treatment group in the absence of that intervention. This is challenging without a randomized experiment and further complicated when few units (possibly only one) are treated. Nevertheless, when data are available on units over time, synthetic control (SC) methods provide an opportunity to construct a valid comparison by differentially weighting control units that did not receive the treatment so that their resulting pre-treatment trajectory is similar to that of the treated unit. The hope is that this weighted ``pseudo-counterfactual" can serve as a valid counterfactual in the post-treatment time period. Since its origin twenty years ago, SC has been used over 5,000 times in the literature (Web of Science, December 2025), leading to a proliferation of descriptions of the method and guidance on proper usage that is not always accurate and does not always align with what the original developers appear to have intended. As such, a number of accepted pieces of wisdom have arisen: (1) SC is robust to various implementations; (2) covariates are unnecessary, and (3) pre-treatment prediction error should guide model selection. We describe each in detail and conduct simulations that suggest, both for standard and alternative implementations of SC, that these purported truths are not supported by empirical evidence and thus actually represent misconceptions about best practice. Instead of relying on these misconceptions, we offer practical advice for more cautious implementation and interpretation of results.

Whether the complex numbers of standard quantum theory are experimentally indispensable has remained open for decades. Real quantum theory (RQT), obtained by replacing complex amplitudes with real ones while retaining the usual Kronecker-product composition rule, reproduces all single-party and bipartite Bell correlations of quantum theory (QT), but its lack of local tomography suggested that the two theories might diverge in more general local experiments. This possibility appeared to be confirmed by Renou et al., who argued that a bilocal network experiment can falsify RQT without falsifying QT. Here we show that this conclusion relies on an experimentally untestable assumption. The key distinction is between product-state independence, which constrains the mathematical form of source states, and operational independence, which is defined entirely by the absence of observable cross-source correlations. We prove that, once source independence is imposed operationally, every finite network correlation achievable in QT is also achievable in RQT with the same locality structure of the measurements. We then extend this equivalence to arbitrary finite sequential multipartite protocols involving channels and measurements with prescribed locality structure. Thus, as long as no violation of QT is observed, RQT cannot be experimentally falsified. Our results restore the empirical indistinguishability of QT and RQT, while showing that they support markedly different pictures of the correlation structure underlying the same observed world.

Interfacial hydrodynamic instabilities in multicomponent superfluids provide a versatile platform to explore nonequilibrium quantum dynamics beyond classical fluid analogues. We study dynamical interfacial instabilities in a quasi-two-dimensional three-component Bose-Einstein condensate confined in a harmonic trap, where rotation is applied selectively to the intermediate component to generate controlled relative motion at two interfaces. This selective rotation protocol enables the independent tuning of shear and counterflow across the inner and outer boundaries, allowing direct control over the nature and strength of the resulting instability mechanisms. Three regimes are examined: Kelvin-Helmholtz instability in the strongly immiscible limit, counter-superflow instability in the partially miscible regime, and a parameter window where both unstable mechanisms are present. The onset condition for the Kelvin-Helmholtz instability is derived using a hydrodynamic pressure-balance approach, and the subsequent nonlinear evolution is obtained from time-dependent Gross-Pitaevskii simulations. A Bogoliubov-de Gennes analysis is performed to identify the dominant unstable modes excited during the dynamical evolution of the system. The conniving features of the collective excitations and their spatial structures have been consistent with the density modulations observed during the dynamics. The results demonstrate that the presence of two interfaces and tunable intercomponent interactions in a three-component condensate modifies the instability mechanisms relative to binary mixtures and provides a controlled parameter regime to study multicomponent quantum hydrodynamics.

We introduce a measurement-induced quantum neural network (MINN), an adaptive monitored-circuit architecture in which mid-circuit measurement outcomes determine the entangling gates in subsequent layers. In contrast to standard monitored circuits where sites and gates are sampled randomly, the gates are parametrized and variational, producing correlated history-dependent dynamics and injecting nonlinearity through measurement back-action. A generic MINN is not expected to be efficiently classically simulable. To demonstrate feasibility, we study a matchgate MINN that admits exact fermionic simulation and can be trained with gradient estimators. We apply the architecture to continuous optimization, image classification, and ground-state search in the Sherrington-Kirkpatrick spin glass, finding effective training and performance over a broad range of monitoring rates.

Large Eddy Simulations with flamelet-based thermochemistry are used to investigate the behaviour of a premixed hydrogen-air flame stabilised by a bluff-body. Validation against experimental data is carried out first to demonstrate the model's ability to predict both velocity field and flame structure. The capability of the model in predicting differential diffusion effects is then assessed, in particular regarding the coupling between differential diffusion, tangential strain and curvature, and their effect on mixture fraction redistribution and reaction rate variation. Results indicate that unstretched flamelet thermochemistry is capable of capturing the increase in mixture fraction caused by positive resolved strain, as well as negative variations of mixture fraction due to negative curvature. Furthermore, the model is observed to mimic the effects of negative Markstein length to a certain extent, so that positive tangential strain causes reaction rate increase. The interplay between resolved stretch and preferential diffusion is also shown to lead to a shorter flame length which is in better agreement with experimental observations as compared to simulations under unity Lewis number assumption. These findings highlight that the macroscopic effects of differential diffusion and stretch on the premixed hydrogen flame, characterised by significant strain levels, can be predicted using a flamelet-based approach and without recurring to strained flamelets database, which implies important simplifications in the combustion modelling of turbulent hydrogen-premixed flames and offers valuable insights for the design of novel combustors.

User interface personalization enhances digital efficiency, usability, and accessibility. However, in user-driven setups, limited support for identifying and evaluating worthwhile opportunities often leads to underuse. We explore a reflexive personalization approach where individuals engage with their digital interaction data to identify meaningful personalization opportunities and benefits. We interviewed 12 participants, using experimental vignettes as design probes to support reflection on different forms of using interaction data to empower decision-making in personalization and the preferred level of system support. We found that people can independently identify personalization opportunities but prefer system support through visual personalization suggestions. Interaction data can shape how users perceive and approach personalization by reinforcing the perceived value of change and data collection, helping them weigh benefits against effort, and increasing the transparency of system suggestions. We discuss opportunities for designing personalization software that raises end-users' agency over interfaces through reflective engagement with their interaction data.

The role of tetraquark operators in studying the isodoublet strange $κ$ and isovector nonstrange $a_0(980)$ scalar mesons in lattice QCD is examined using an ensemble with $m_π\approx230$ MeV and spatial extent $L$ such that $m_πL\approx4.4$. Hermitian correlation matrices using both single-meson, meson-meson, and tetraquark interpolating operators are used to extract the spectrum of finite-volume stationary states in the appropriate symmetry channels. Hundreds of local and extended tetraquark operators are explored. Determinations of the spectrum in each channel are found to be unreliable without the inclusion of at least one tetraquark operator. For example, the inclusion of tetraquark operators with isospin 1/2 and strangeness 1 quantum numbers reveals the existence of an additional energy level in the $Kη$ sub-system below the $Kη$ threshold. The implications of this on parametrizing the scattering $K$-matrix through a well-known quantization condition to extract properties of the $κ$ and $a_0(980)$ scalar meson resonances are discussed.

We numerically simulate the two-dimensional inertial flow with the van der Waals effect in a straight periodic channel around the Poiseuille and Couette stationary states. Even though the flow remains laminar macroscopically, we observe complex dynamics and power decay of the Fourier spectra of small fluctuations of the density, velocity divergence, vorticity and kinetic energy of the flow near their respective stationary background states. Remarkably, pinning the vorticity to its background state, and leaving only the density and velocity divergence as the variables, results in the dynamics and power decay of the Fourier spectra qualitatively similar to those of the full system. This strongly indicates that the underlying physics of the power spectra reside primarily in the density and velocity divergence variables, and are not directly related to the vorticity of the flow.

Autonomous driving (AD) requires safe and reliable decision-making among interacting agents, e.g., vehicles, bicycles, and pedestrians. Multi-agent reinforcement learning (MARL) modeled by Markov games (MGs) provides a suitable framework to characterize such agents' interactions during decision-making. Nash equilibria (NEs) are often the desired solution in an MG. However, it is typically challenging to compute an NE in general-sum games, unless the game is a Markov potential game (MPG), which ensures the NE attainability under a few learning algorithms such as gradient play. However, it has been an open question how to construct an MPG and whether these construction rules are suitable for AD applications. In this paper, we provide sufficient conditions under which an MG is an MPG and show that these conditions can accommodate general driving objectives for autonomous vehicles (AVs) using highway forced merge scenarios as illustrative examples. A parameter-sharing neural network (NN) structure is designed to enable decentralized policy execution. The trained driving policy from MPGs is evaluated in both simulated and naturalistic traffic datasets. Comparative studies with single-agent RL and with human drivers whose behaviors are recorded in the traffic datasets are reported, respectively.

Camera pipelines receive raw Bayer-format frames that need to be denoised, demosaiced, and often super-resolved. Multiple frames are captured to utilize natural hand tremors and enhance resolution. Multi-frame super-resolution is therefore a fundamental problem in camera pipelines. Existing adversarial methods are constrained by the quality of ground truth. We propose GenMFSR, the first Generative Multi-Frame Raw-to-RGB Super Resolution pipeline, that incorporates image priors from foundation models to obtain sub-pixel information for camera ISP applications. GenMFSR can align multiple raw frames, unlike existing single-frame super-resolution methods, and we propose a loss term that restricts generation to high-frequency regions in the raw domain, thus preventing low-frequency artifacts.

We investigate how the defining statistical features of three-dimensional turbulence respond to systematic reductions of the Fourier-space triadic interaction network. Using direct numerical simulations of both fractally and homogeneously decimated Navier-Stokes dynamics, we show that progressive thinning of the set of active modes leads to a systematic suppression of intermittency and, most strikingly, to the vanishing of the mean dissipation rate in the large-Reynolds-number limit. Structure-function exponents collapse onto their dimensional values, the multifractal singularity spectrum contracts, and the analyticity width extracted from the exponential spectral tail increases monotonically with decimation-each indicating a substantial regularization of the velocity field. Together, these results provide direct evidence that anomalous dissipation in incompressible turbulence is not a generic property of the Navier-Stokes equations, but instead requires the full combinatorial richness of their triadic nonlinear interactions.

In this work, we investigate the structural, magnetic, and microwave magnetic dynamics of multilayered \([{\rm LSMO}/{\rm SRO}]_n\) heterostructures \((n = 1 \text{ and } 5)\) grown on SrTiO\(_3\) (001) substrates. X-ray diffraction confirms high crystallinity and atomically sharp interfaces. Magnetic measurements reveal strong interfacial magnetic coupling, with a distinct two-step magnetization switching observed in the \(n = 5\) heterostructure, while this feature is significantly suppressed in the \(n = 1\) structure. Ferromagnetic resonance (FMR) analysis shows a broad linewidth, pronounced positive magnetic anisotropy, and Gilbert damping on the order of \(10^{-2}\), with damping decreasing as the number of multilayer repetitions increases. These observations demonstrate that Ru--Mn exchange coupling at the interface critically governs the magnetic response and dynamic behavior of the system. The tunable switching and damping properties highlight such oxide heterointerfaces as promising platforms for exploring spin textures, magnetic domain behavior, and room-temperature spintronic applications.

We review recent progress in the understanding of the preheating stage of Higgs inflation formulated within the Einstein-Cartan framework of gravity. This setup smoothly interpolates between the metric and Palatini formulations of the theory, leading to a distinctive phenomenology in an intermediate regime. Following the end of inflation, the Higgs field undergoes a non-trivial out-of-equilibrium evolution driven by tachyonic instabilities and nonlinear self-interactions, which fragment the inflaton condensate and give rise to well-localized oscillon configurations. While early studies suggested the formation of long-lived oscillons and the possibility of an extended matter-dominated phase, more recent analyses show that self-interactions at small field values render these objects transient, eventually triggering their decay and the onset of radiation domination. We discuss the implications of this dynamics for the thermal history of the Universe, the inflationary observables, and the generation of stochastic gravitational waves.

The inclusion of a mass-like term for the gluon in Yang-Mills theories quantized in the Landau gauge has proven to be an effective way of reproducing lattice results for gauge-fixed correlation functions within perturbative computations. Since those quantities are gauge dependent, it is natural to question how general this prescription is for describing the infrared behavior of gluon and Faddeev-Popov ghost propagators in different gauges. In this work, we provide a systematic investigation of this issue in the maximal Abelian gauge, which cannot be deformed into the Landau gauge and has been investigated in gauge-fixed lattice simulations. We compute the one-loop non-Abelian and diagonal gluon propagators and perform fits to lattice data in the case of $SU(2)$. Our results show that the transverse component of the non-Abelian gluon propagator as well as the diagonal gluon propagator, are in good agreement with lattice data in the infrared.

We prove sharp $δ$-discretised versions of some variants of the Furstenberg set problem under weaker or different non-concentration assumptions compared to previous works.