The aim of this paper is to introduce a Mapping-based Hard-constrained Physics-Informed Neural Network (MH-PINN) for efficiently and accurately solving unbounded wave problems. First, we propose a coordinate mapping technique that compactifies the infinite physical domain into a finite computational space. This effectively resolves the sampling difficulties inherent to standard PINNs in unbounded regions. Additionally, it avoids the artificial truncation errors introduced by traditional methods such as perfectly matched layers. Second, we design a physics-based hard-constrained network structure that automatically satisfies both the inner boundary conditions and the far-field radiation conditions. This structure eliminates boundary loss terms, yielding high computational efficiency and fast convergence, which effectively addresses the challenges of high-frequency problems. Third, we introduce an inverse factor correction for boundary coefficients to address the influence of asymptotic factors,which makes the method highly geometrically adaptable. Finally, we present numerical examples covering various acoustic radiation and scattering scenarios as well as elastic dynamics scenarios to demonstrate the efficiency and accuracy of our algorithm.It highlights its potential for broader applications in the field of computational wave dynamics.

The onset of life is often framed around membrane bound compartments and encoded metabolism, leaving unresolved how spatial organization arose before stable boundaries. In this context, environmental gradients are usually treated as boundary conditions rather than variables structuring chemical dynamics. We ask whether spatial localization and functional coupling can emerge under realistic environmental gradients in the absence of membranes, proposing that spatial variations in energy availability act as organizing variables that bias transport and reaction. We introduce a reaction diffusion model in which interacting chemical species evolve within an externally imposed activity landscape defined by coupled gradients in pH, redox potential and temperature, integrating diffusion, gradient driven drift and position dependent reaction kinetics. We performed simulations across a range of gradient strengths representative of hydrothermal vent like conditions. Our results suggest that sufficiently strong gradients induce spontaneous accumulation of reactants, spatial alignment of reaction maxima and the emergence of stable, confined chemical states. Localization arises above a threshold at which gradient driven transport overcomes diffusive and degradative losses. We conclude that spatially structured energy landscapes can support organized chemical dynamics without predefined compartments, providing a mechanism for coupling and persistence in continuous media. Potential applications include experimental platforms for studying prebiotic chemistry, microfluidic systems with controlled gradients and the design of chemically responsive materials.

The Lorentzian type IIB matrix model is a promising candidate for a nonperturbative formulation of superstring theory. In this model, the eigenvalue distribution of the $N\times N$ bosonic matrices $A_μ$ $(μ= 0 , \ldots , 9)$ represents an emergent spacetime, which is determined by the dynamics of the model in the large-$N$ limit. Here we perform numerical simulations of the model overcoming the sign problem by the complex Langevin method with the matrix size $N$ up to $128$. In order to avoid the singular drift problem due to the Pfaffian, which appears after integrating out the fermionic matrices, we deform the model in a manner inspired by the supersymmetric deformation, which is used to define the ``polarized type IIB matrix model'' in the Euclidean case. We find that the deformed model exhibits a phase in which (3+1)-dimensional expanding spacetime emerges with both space and time being smooth and real.

Financial firms have gone through three major technological waves: computerization in the 1980s and 1990s, the rise of indexing and passive investing in the 2000s and 2010s, and the AI and automation wave from roughly 2015 to the present. This project studies how much labor is required to manage capital across those waves by tracking a simple productivity measure: assets under management per employee. Using a small panel of representative firms, we compare changes in AUM per employee, revenue per employee, and operating expense intensity over time. The goal is not to identify causal effects, but to document stylized facts about how technology changes the scale of asset management work.

In this study, we propose an extension of general relativity inspired by the van der Waals equation of state, incorporating non-ideal thermodynamic effects into the gravitational sector. Our approach is based on the thermodynamic interpretation of gravity introduced by Jacobson, in which the field equations arise from the Clausius relation. Within this framework, we obtain modified gravitational field equations in which the effective gravitational coupling is no longer constant, but instead evolves with the properties of the underlying spacetime system. This dynamical behavior leads to significant consequences in high-energy regimes. In particular, it provides a natural mechanism for avoiding the initial singularity of standard Big Bang cosmology and gives rise to non-singular black hole solutions. These findings indicate that incorporating non-ideal thermodynamic features into the description of spacetime may offer a consistent route toward resolving fundamental singularities in classical gravitational theory.

The General Antiparticle Spectrometer (GAPS) is an Antarctic stratospheric balloon mission designed to provide unmatched sensitivity to low-energy (<0.25 GeV/n) cosmic-ray antiprotons, antideuterons, and antihelium nuclei as signatures of dark matter. The distinctive GAPS particle identification technique relies on measuring the energy loss along the track of an incoming antinucleus as it slows down and is captured into an exotic atom, and then detecting the de-excitation X-rays and the nuclear annihilation products. This measurement is realized using a Tracker composed of more than 1000 custom silicon strip detectors and a plastic scintillator time-of-flight (TOF) system instrumenting more than 40m$^2$. Together, these subsystems provide the velocity and energy resolution, stopping power, particle tracking, and X-ray identification necessary to distinguish rare antinucleus signals from the abundant positive-nucleus backgrounds, all within the constraints of a high-altitude mission. A multi-loop capillary heat pipe system has been developed to maintain the tracker operating temperature with significant mass and power savings over a conventional pump-based system. The first GAPS science payload flew for 25 days during the 2025/26 NASA Antarctic balloon campaign. We detail the design, integration, and commissioning of the payload prior to flight.

Although quantum transport at the nanoscale has received widespread attention since Landauer's pioneering work in 1957, we remark, that a general theory that sheds light on the difference between classical and quantum relativistic physical models is still lacking. By considering a classical 3D gas of non-interacting quasi.particles, the article presents a unified theory that provides a generalized conductance of dimensionless quasi-particles, neutral massive, electric, thermal, and photon currents. The investigation begins with an analogy between the original Drude model of 1900 and a modified Drude model of quasi-particles, which includes a ballistic transport regime and is independent of statistics (excluding Bose-Einstein condensation). Next, we construct connections between the quasi-particle unit in the modified Drude model and the carrier unit in dimensionless, electric, massive neutral, phonon, and photon currents. By establishing a connection between Planck's constant $h$ and a classicaò action that takes into account the correct statistics, $h_s$, we derive the fundamental quantum unit of conductance for any of the mentioned currents. We further extend the diffusion coefficient of quasi-particles from the classical regime to the quantum and relativistic regimes.

The increasing complexity of embedded software has made comprehensive manual testing impractical, motivating the use of automated techniques such as fuzzing. Coverage-guided fuzzers like AFL++ have shown strong results for conventional software but remain challenging to apply effectively in embedded contexts, where peripheral behaviors play critical roles. Existing approaches either use fast user-mode simulators, sacrificing peripheral realism, or rely on full-system simulators with manual instrumentation, limiting applicability to large-scale software. In this work, we present a novel framework that integrates AFL++ with a stateful SystemC-TLM virtual prototype to enable realistic fuzzing of embedded software. Fuzzer-generated inputs are injected directly into peripheral models, allowing peripherals to trigger natural side effects such as interrupts and FIFO updates. By integrating fuzzing with full-system simulation, our framework advances the effectiveness of pre-silicon testing for embedded systems. Results on embedded workloads show that our approach eliminates false positives while maintaining comparable code coverage and execution performance as state-of-the-art tools.

The maintained artifact in an AI-enabled system is not code plus settings, but a versioned governed program space: domains, structural constraints, eligibility, evaluation assets, and a statistical release gate. AI-enabled systems operate under changing world conditions: provider models and APIs change, input distributions drift, evaluation sets age, and objectives such as quality, cost, latency, and safety are renegotiated over time. In practice, teams often respond through ad hoc changes to model choice, retrieval policy, prompt structure, and operational thresholds. Fixed-assignment reasoning is therefore insufficient: a chosen assignment is valid only relative to an environment, evaluation set, and policy state. We argue that such choices should be treated as tuned variables: program variables maintained under governance as environments and evaluation sets evolve. Building on SE4AI work and our prior work on governed tuning, this paper positions the governed space as the software-engineering object. Here, statistical means that promotion relies on sampled evaluation sets, estimated evidence, effect-size margins, and confidence/risk thresholds.

Despite the rapid advancement of generative agents, their deployment in real-world industry scenarios often encounters significant challenges due to a lack of domain-specific knowledge. To address this gap, we present KnowPilot: a Domain-Specific Knowledge Augmented Generative Agent System. KnowPilot is an open-source framework that integrates task-specific priors, explicit knowledge, and experiential knowledge to enhance agent performance in specialized applications. It combines knowledge retrieval from structured repositories with a memory system capable of capturing expert experience through human AI interaction. Taking domain-specific writing generation as a representative case, KnowPilot enables private deployment, supports injection of task requirements, loads private knowledge bases, and stores tacit expert knowledge as persistent memory. Experimental results demonstrate that KnowPilot achieves superior performance in domain-oriented text generation and is applicable across fields such as medicine, finance and industry.

Enterprise hiring systems generate data across multiple disconnected platforms: applicant tracking systems (ATS) record candidate profiles, human resource information systems (HRIS) record performance outcomes, and behavioral assessments capture personality and behavioral dimensions. Each system operates independently, and the reasoning behind hiring decisions is lost when managers retire, transfer, or leave. Decision traces are structured evidence chains connecting screening inputs, assessment signals, and production outcomes. They have been theorized but never operationalized at production scale. We present, to our knowledge, the first such study: a deployment at a Fortune 500 insurance carrier (N=10,765 agents hired, 2022-2025), where connecting three siloed data systems produced three findings. First, of 8,181 unique skills parsed from ATS profiles (3,597 testable), not a single keyword predicts production after Bonferroni correction; 30 are significantly anti-predictive, and the median keyword is associated with 25% lower odds of production. Requiring insurance experience alone would reject 2,863 agents who produced $17.7M in annual premium credit. Second, personality-based behavioral assessment (Predictive Index) achieves AUC=0.647 standalone and AUC=0.735 when fused with ATS and behavioral scoring data. Third, speed-to-production follows a measurable economic constant of $54/day per agent unadjusted, or $35/day controlling for source channel and tenure, moderated by behavioral score: high-scored agents capture $114/day from speed acceleration versus $41/day for low-scored agents. These findings were invisible within any single system. We discuss implications for hiring system design, the limitations of keyword-based screening, and the conditions under which institutional knowledge can be captured and operationalized.

Agentic AI systems plan, use tools, maintain state, and act across multi-step workflows with external effects, meaning trustworthy deployment can no longer be judged by task completion alone. The current literature remains fragmented across benchmark-centered evaluation, standards-based governance, orchestration architectures, and runtime assurance mechanisms. This paper contributes a bounded evidence synthesis across a manually coded corpus of twenty-four recent sources. The core finding is a governance-to-action closure gap: evaluation tells us whether outcomes were good, governance defines what should be allowed, but neither identifies where obligations bind to concrete actions or how compliance can later be proven. To close that gap, the paper introduces three linked artifacts: (1) a four-layer framework spanning evaluation, governance, orchestration, and assurance; (2) an ODTA runtime-placement test based on observability, decidability, timeliness, and attestability; and (3) a minimum action-evidence bundle for state-changing actions. Across sources, evaluation papers identify safety, robustness, and trajectory-level measurement as open gaps; governance frameworks define obligations but omit execution-time control logic; orchestration research positions the control plane as the locus of policy mediation, identity, and telemetry; runtime-governance work shows path-dependent behavior cannot be governed through prompts or static permissions alone; and action-safety studies show text alignment does not reliably transfer to tool actions. A worked enterprise procurement-agent scenario illustrates how these artifacts consolidate existing evidence without introducing new experimental data.

We present a review of supersymmetry, supergravity, and the non-perturbative dynamics of gauge theories, tracing a path from the supersymmetry algebra to moduli stabilisation and de~Sitter vacua in string theory. Representations of the supersymmetry algebra, the superspace formalism, and basic models including the Wess--Zumino model and $\mathcal{N}=1$ supersymmetric Yang--Mills theory are discussed. The non-perturbative dynamics of $\mathcal{N}=2$ gauge theories is analysed through the Seiberg--Witten solution: the curve, prepotential, Picard--Fuchs system, BPS spectrum, and confinement via monopole condensation. The transition to $\mathcal{N}=1$ supergravity is carried out in three steps, showing how the Kähler potential $K$ and superpotential $W$ determine all five Lagrangian sectors and how the scalar potential acquires its exponential prefactor and gravitationally induced negative contribution. String theory applications include D-brane gauge theories, the AdS/CFT correspondence, geometric engineering of the Seiberg--Witten solution, and reduction of $\mathcal{N}=4$ to $\mathcal{N}=1$ supersymmetry. The KKLT moduli stabilisation mechanism is analysed in detail, including $α'^3$ corrections to the Kähler potential. Three regimes of the scalar potential are identified -- classical KKLT, corrected KKLT with a shifted AdS minimum, and a runaway regime -- and the critical parameter $\hatξ_c$ separating controlled de~Sitter vacua from decompactification is determined. The tension with the de~Sitter swampland conjecture is discussed.

High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. This paper presents Quantum Integrated High-Performance Computing (QHPC), a visionary architectural framework that unifies CPUs, GPUs, FPGAs, and QPUs as first-class heterogeneous resources. We propose a layered system design comprising unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of our vision is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.

As automated vehicles (AVs) enter mixed traffic, proactively anticipating the evolution of human driving behavior during critical interactions, such as lane changes, is essential. However, classical Evolutionary Game Theory (EGT) fails to capture the complexity of human decision-making during lane changes. Specifically, by strictly assuming independence between agents, classical models calibrated on empirical payoffs predict a convergence to unrealistic full cooperation, contradicting the stable 42% cooperation rate observed in real-world data. To resolve this discrepancy, this study introduces a Quantum Game Theory (QGT) framework. We analyze 7,636 lane-changing interactions from the Waymo Open Motion Dataset (WOMD) to derive empirical payoff matrices via a Quantal Response Equilibrium (QRE) model. Utilizing the Marinatto-Weber (MW) quantization scheme, we introduce an entanglement parameter to mathematically embed latent correlations directly into the payoff structure of a single interaction. Our results identify a human entanglement parameter of $|b|^2_{HDV} \approx 0.52$ that accurately reproduces the observed mixed equilibrium. Furthermore, simulations of three AV deployment strategies (classical, entangled, and inverted) reveal that human adaptation depends critically on the underlying AV algorithm: while cooperative classical AVs maximize system-wide cooperation at high market penetration rates, defective inverted AVs paradoxically yield higher overall cooperation at low penetration rates by prompting more cooperative behaviors from human drivers. Consequently, rather than waiting for large scale deployment to observe these effects, stakeholders can utilize this framework to simulate repeated interactions and proactively anticipate how human driver behavior will evolve in response to specific AV software designs.

This paper presents an efficient method based on Evolutionary Center Algorithm (ECA) for accurately and efficiently determining the optimal reaction and diffusion parameters for Chemical-Diffusive Models (CDM) to simulate flame acceleration (FA) and deflagration-to-detonation transition (DDT). The proposed method leverages the global search capability of the ECA and the local optimization strength of the Nelder-Mead (NM) algorithm. The hybrid approach (ECA-NM) can efficiently optimize CDM parameters that are capable of accurately reproducing the major properties of combustion waves. The CDMs for premixed flames and detonations of hydrogen in air or oxygen were developed using the present ECA-NM method and validated against canonical tests of combustion waves and previous experiments of FA and DDT. The results show that the major flame and detonation properties calculated using the developed CDMs match those obtained from detailed chemical reaction mechanisms over a wide range of equivalence ratio. The simulated FA and DDT in a channel also agree qualitatively and quantitatively with experiments in terms of complex flame instabilities (e.g., tulip and distorted tulip flames), flame displacement speed, and detonation occurrence. In addition, detailed comparisons to the traditional genetic algorithm demonstrate that the developed ECA-NM method diminishes the global error by four orders of magnitude while reducing the computational cost by two orders of magnitude. This work provides a significantly efficient method for developing chemical-diffusive models that allows quantitative multi-scale simulations of transient flames and detonations in complex scenarios.

Infectious diseases continue to pose significant public health challenges worldwide, requiring effective prevention and control strategies to mitigate their negative impact. Infectious diseases can be broadly classified into two groups: vaccine-preventable diseases (e.g., measles, polio, influenza, hepatitis B, pneumonia) and vaccine-non-preventable diseases (e.g., HIV/AIDS). Vaccine-preventable disease models are one of the essential tools for understanding infectious disease dynamics, evaluating intervention strategies, and guiding public health policies. In this review article, we explore the recent advancements in modeling two particular vaccine-preventable infectious diseases. Here, we consider both deterministic and stochastic models to comprehensively capture the complexity of disease transmission, vaccine efficacy, and population-level immunity. We highlight the application of these models to the infectious diseases, namely, bacterial and viral pneumonia caused by the bacteria Streptococcus pneumoniae (S. pneumoniae) and the respiratory syncytial virus (RSV). Pneumonia carry a substantial global burden, where modeling has played a crucial role in assessing vaccine impacts and optimizing immunization strategies to minimize the disease burden. By synthesizing recent methodologies and findings, this review provides valuable insights for future research and policy decisions aimed at improving vaccine-preventable disease control for pneumonia caused by S. pneumoniae and RSV.

Accurately treating strong electron correlation in quantum chemistry typically requires multireference wave-function methods with steep computational scaling. The recently proposed i-DMFT method promises near configuration-interaction accuracy at mean-field cost by invoking an empirical linear relation between correlation energy and entropy (Collins' conjecture), whose validity remains unclear. We systematically assess this relation across a range of di- and polyatomic molecules, including diverse bond types, third-row elements, different types of geometric distortions, and excited states. We find that the conjectured linearity holds for bond-breaking processes dominated by electron redistribution within orbital pairs, but breaks down for heterolytic dissociation and excited states. In simple molecules, i-DMFT provides a reasonable description of total energies, but does not reliably reproduce reduced density matrices or individual energy components. It further degrades in more complex cases such as ethylene. Based on these results, we formulate criteria for the validity of the conjecture and outline implications for entropy-based reduced density matrix functionals.

The sensitivity of experiments searching for the electron electric dipole moment (eEDM) using the symmetric top molecules can be greatly enhanced by laser cooling. A detailed understanding of the Zeeman structure of the eEDM-sensitive levels is crucial for controlling systematic effects. We have developed a method for calculating the $g$-factors of $K$-doublet levels in symmetric top molecules and applied it to RaOCH$_3$. The electric-field-dependent $g$-factors of the first excited rotational level of RaOCH$_3$ are calculated. $K$-doublet levels with a small difference in $g$-factors are identified, and the main contributions to this difference are determined.

This study extends the Gai-Kapadia framework, originally developed for interbank contagion, to assess systemic risk and default cascades in global equity markets. We analyze a 30 asset network comprising Brazilian and developed market equities over the period 2015-2026, constructing exposure based financial networks from price co-movements. Threshold filtering (theta = 0.3 and theta = 0.5) is applied to isolate significant interconnections. Cascade dynamics are analyzed through a combination of deterministic propagation and stochastic Monte Carlo simulations (n = 1000) under varying shock intensities. The results show that the system exhibits strong global resilience, with a negligible probability of large scale failure, while maintaining localized vulnerability within highly clustered subnetworks. In particular, shocks lead to an average of 1.0 failed asset for single shocks and 2.0 for simultaneous shocks, indicating limited propagation below a critical threshold. Network analysis reveals a clear structural asymmetry: Brazilian assets display high clustering (Ci approx 0.8-1.0) and dense connectivity, which amplifies local shock propagation, whereas developed market assets exhibit lower connectivity (Ci approx 0.2-0.5), limiting systemic spread. Tail risk analysis, based on empirical CCDF and Hill estimators, confirms the presence of heavy tailed loss distributions, particularly in emerging markets, reinforcing their exposure to extreme events. These findings demonstrate that systemic risk arises from the interaction between network topology and tail behavior, rather than from isolated asset characteristics. The proposed framework provides a scalable and empirically grounded approach for stress testing and systemic risk assessment, offering relevant insights for regulators and portfolio managers in increasingly interconnected financial markets.

Kubernetes offers two default paths for scaling Nodejs workloads, and both have structural limitations. The Horizontal Pod Autoscaler scales on CPU utilization, which does not directly measure event loop saturation: a Node.js pod can queue requests and miss latency SLOs while CPU reports moderate usage. KEDA extends HPA with richer triggers, including event-loop metrics, but inherits the same reactive control loop, detecting overload only after it has begun. By the time new pods start and absorb traffic, the system may already be degraded. Lowering thresholds shifts the operating point but does not change the dynamic: the scaler still reacts to a value it has already crossed, at the cost of permanent over-provisioning. We propose a predictive scaling algorithm that forecasts where load will be by the time new capacity is ready and scales proactively based on that forecast. Per-instance metrics are corrupted by the scaler's own actions: adding an instance redistributes load and changes every metric, even if external traffic is unchanged. We observe that operating on a cluster-wide aggregate that is approximately invariant under scaling eliminates this feedback loop, producing a stable signal suitable for short-term extrapolation. We define a metric model (a set of three functions that encode how a specific metric relates to scaling) and a five-stage pipeline that transforms raw, irregularly-timed, partial metric data into a clean prediction signal. In benchmarks against HPA and KEDA under steady ramp and sudden spike, the algorithm keeps per-instance load near the target threshold throughout. Under the steady ramp, median latency is 26ms, compared to 154ms for KEDA and 522ms for HPA.

As IPv6 deployment accelerates, understanding the evolving security posture of network peripheries becomes increasingly important. A DSN 2021 study introduced the first large-scale discovery of IPv6 network peripheries, uncovering risks like service exposure and routing loops. However, its scope was limited to three regions and is now outdated. In this paper, we revisit and significantly expand upon that work, presenting a comprehensive, up-to-date security assessment of IPv6 network peripheries. To support efficient large-scale scanning, we propose a novel Response-Guided Prefix Selection (RGPS) strategy to identify high-value IPv6 prefixes for probing. Our global-scale measurement covers 73 countries/regions and identifies over 281.9M active IPv6 network peripheries, including a 371.2% increase (245M) over the 52M reported in 2021 for India, China, and America. Our service exposure analysis shows that 2.5% of reachable services are still dangerously exposed, including outdated administrative interfaces and misconfigured servers, while correlation with known CVEs reveals recurring software vulnerabilities. Building on this service-exposure perspective, we further design a Hierarchical LLM Exposure Verification (HLEV) framework to identify unauthorized-access risks in exposed LLM deployment tools, revealing multiple security weaknesses caused by insecure default configurations and missing authentication. Additionally, we revisit routing loop vulnerabilities and identify 4.5M loop-prone responses, confirming that flawed routing behaviors remain widespread across vendors and countries/regions. These findings suggest that while IPv6 adoption has surged, key security challenges persist and are structurally embedded.

Heterogeneity in individual characteristics and behaviour is a fundamental property of complex dynamical systems. While previous studies on evolutionary dynamics of strategies evolution in various systems have predominantly focused on the structural heterogeneity, dynamical heterogeneity in individuals' strategy update has been largely neglected. Here, we introduce a novel dynamical update mechanism based on individuals' decision-making information, comprising personal and social components. This update rule allows each individual to vary in the weight of personal information and the amount of social information, capturing the general scenario of dynamically heterogeneous populations. We find that cooperation, as a collective prosocial outcome, is significantly enhanced when highly connected individuals on interaction network rely more heavily on personal information and access more social information. This effect is notably absent in homogeneous networks, thereby overturning the prevailing consensus that structural heterogeneity inherently suppresses cooperation. This theoretical prediction is further validated by empirical evidence from GitHub collaboration networks. Furthermore, individuals preferentially linking to those who are well-informed and possess greater personal information further promotes collective cooperation. We additionally reveal that cooperators gain a decisive advantage when relying more on personal information compared to defectors, whereas social information affects cooperators and defectors equivalently. Our findings offer profound insights into how dynamical heterogeneity fundamentally shapes the evolution of collective cooperation in complex systems.

Inductive definitions are an important form of knowledge. The logic FO(ID) is an extension of classical first-order logic FO with general non-monotone inductive definitions. Most existing proof systems for inductive definitions impose syntactic constraints on their definitions, thereby excluding many useful and natural definitions. We extend an existing sequent calculus LKID by Brotherston and Simpson, founded on the principle of mathematical induction, to a sequent calculus SCFO(ID) for FO(ID). The main challenge in this extension is the accommodation of non-monotone inductive definitions. To overcome this challenge, we draw inspiration from the stable semantics, which is a commonly used semantics in logic programming that is closely related to the well-founded semantics behind FO(ID). We corroborate SCFO(ID) by establishing several proof-theoretical properties and through demonstration on various examples. In conclusion, SCFO(ID) is a theoretically substantiated sequent calculus for FO(ID), enabling formal proofs of theorems involving general inductive definitions.

The next 20 years will be the golden age of flavour physics, with the operation of the LHCb and Belle II experiments. After that an $e^+e^-$ collider could further improve the precision with sizeable $Z$, $W^+W^-$ and $t\bar{t}$ runs.

Massive stars commonly form binaries that can evolve into compact systems via common envelope evolution (CEE), a critical but poorly understood phase -- especially when the companion is a neutron star. Understanding the drag force exerted on a neutron star during CEE is a key to the quantitative evaluation of orbital decay, merger timescale, and compactness of the resultant binary. In this paper, we conduct general-relativistic hydrodynamical simulations under a novel strategy of multi-layer domain-decomposition to treat the vast disparity of $10^4$--$10^7$ between the neutron star radius and the accretion radius. Our 10-model survey spans diverse physical conditions that the neutron star encounters in the envelope of a massive star. We find that nested bow shocks with alternating orientations commonly form. This configuration is qualitatively different from those in the conventional picture and results in an enhancement of the drag force by one to two orders of magnitude from what the Bondi--Hoyle--Lyttleton formula predicts. Moreover, the direction of the net force can reverse depending on the envelope conditions, contrary to the standard picture in which the drag always decelerates the companion. These results will serve as a basis for improvements of the drag force prescription in CEE modeling, and have implications for binary evolution theory.

Physical random number generators based on chaotic microcombs, with their complex nonlinear dynamics and multi-channel parallel capability, have attracted considerable research attention. However, key technical challenges for chaotic microcombs are the high correlation between symmetric teeth and the low bandwidth of single-channel teeth, which seriously affect the speed and scalability of random number generation. We experimentally demonstrate a petabit-per-second (Pbit/s) parallel random number generation system based on intensity chaotic modulation and Rayleigh scattering. Through intensity modulation, the effective bandwidth of the single-channel entropy source is increased from 440MHz to 27.6GHz. Crucially, Rayleigh scattering further contributes through the random superposition of backscattered light, which introduces unpredictable fluctuations in intensity, phase, and polarization. This randomness suppresses inter-channel correlation among parallel entropy sources to ~0.02, ensuring their orthogonality. Moreover, by employing polarization-diverse coherent detection on a single-channel, four new low correlated sub-channels are extracted: X-/Y- intensity and phase. We achieve a single-channel bit rate of 14.336 Tbit/s and a total bit rate of 1.032 Pbit/s (over 72 parallel channels) with offline post-processing, representing the highest post-processing record reported in both the single-channel and the total system. Moreover, our scheme based on a single chaotic microcomb and fiber scattering link show fundamentally scalable. The total bit rate can be significantly pushed beyond the Pbit/s level by further expanding the usable comb channel and/or by deploying multiple fiber scattering links in parallel, paving a practical path toward higher throughput regimes.

Photometric redshift estimation is a key requirement for modern large-area surveys, where spectroscopic measurements are observationally prohibitive. Seyfert II galaxies provide a particularly challenging test case due to the combined effects of nuclear activity, host-galaxy emission, and dust attenuation. In this work, we develop a machine learning approach for photometric redshift estimation using a spectroscopically defined sample of 23,797 Seyfert II galaxies selected from SDSS and cross-matched with WISE. We construct feature sets based on optical, mid-infrared (MIR), and combined optical+MIR broadband colours, and evaluate their performance using different regression models. The best results are obtained with the combined Optical+MIR features and a Random Forest model, reaching NMAD = 0.0188, R 2 = 0.9561, and an outlier fraction of η = 0.294%. The results show that the accuracy is primarily driven by the physical information content of the features and the homogeneity of the sample. The method provides a robust and scalable solution for photometric redshift estimation in upcoming wide-field surveys.

This paper presents the Julia package CCOpt, built on top of the interior-point solver MadNLP. CCOpt implements a suite of algorithms for Mathematical Programs with Complementarity Constraints (MPCCs). The solver additionally comes with interfaces for use in Matlab, Python, and C++. MPCCs have recently gained renewed attention in engineering optimization, as complementarity provides a powerful modeling tool for nonsmooth functions and logical conditions. These problems are inherently challenging since their nonlinear programming reformulations violate classical regularity conditions at all feasible points, complicating both theoretical analysis and numerical treatment. Consequently, specialized algorithms are required to handle this degeneracy, and several approaches have been proposed. We implement a toolbox of methods, including relaxation and penalty approaches, as well as a crossover to recently proposed active-set methods. Our solver is based on nonlinear interior-point algorithms that couple the relaxation or penalty parameter with the barrier parameter, yielding substantial speedups compared to standard implementations. Both monotone and nonmonotone strategies for updating this joint parameter update are proposed and investigated. In addition, we propose regularization techniques that improve the conditioning of the KKT system for small relaxation parameters, enhancing robustness and computational efficiency. The implementation is validated on the classical MacMPEC benchmark, large-scale problems in security-constrained optimal power flow, optimal control of nonsmooth systems, as well as on quadratic programs with complementarity constraints arising in model predictive control. This benchmarking reveals an algorithmically driven improvement of often an entire order of magnitude over other methods, including commercial solvers.

As language models are deployed as autonomous agents that negotiate, cooperate, and compete on behalf of human principals, their strategic dispositions acquire direct economic consequences. Here we show, across 51,906 game-theoretic trials generating 826,990 strategic decisions from 25 large language models spanning seven developers and 38 canonical games, that models converge on competitive and coordination behaviour (coefficient of variation 0.06 for coordination, 0.11 for strategic depth) while diverging 48-fold on cooperation, from 1.5 per cent (GPT-5 Nano) to 71.5 per cent (Claude Opus 4.6). Provider identity is the dominant predictor of cooperative disposition, and this divergence is generationally unstable: OpenAI cooperation fell from 50.3 to 1.5 per cent across four model generations while Google cooperation rose from 8.3 to 56.8 per cent. Endgame analysis reveals that Anthropic frontier models sustain 57 per cent cooperation in the final round of finitely repeated games, where backward induction predicts zero, while the newest Google models cooperate throughout but universally defect when punishment becomes impossible. These strategic personalities are shaped by training pipelines, shift unpredictably across model versions, and cannot be inferred from capability benchmarks, yet they determine the cooperative outcomes of every economic interaction these models mediate. The complete dataset and an interactive explorer for the data are publicly available at https://felipemaffonso.github.io/strategic-personalities/.