Large astronomical instruments using tens to hundreds of optical or infrared science detectors pose specific challenges for detector control, where, in addition to performance, other engineering aspects like scalability, power consumption, size, weight and programmatic aspects such as cost and sustainability need to be considered. In this paper we analyze the approach existing instruments have taken for detector control. We focus this analysis on recent ground based astronomical instruments using 10 or more detectors for science imaging or spectrography. From this analysis we identify key technologies, like cryogenic electronics, Ethernet based interfaces and fully-digital detectors, for implementing efficient control of many detectors. We also propose a concept joining all identified technologies that could be considered for future large ESO instruments as a complement of ESO's general detector controller, NGCII.

Velopharyngeal dysfunction (VPD) is characterized by inadequate velopharyngeal closure during speech and often causes hypernasality and reduced intelligibility. Although speech-based machine learning models can perform well under standardized clinical recording conditions, their performance often drops in real-world settings because of domain shift caused by differences in devices, channels, noise, and room acoustics. To improve robustness, we propose a two-stage framework for VPD screening. First, a nasality-focused speech representation is learned by supervised contrastive pre-training on an auxiliary corpus with phoneme alignments, using oral-context versus nasal-context supervision. Second, the encoder is frozen and used with lightweight classifiers on 0.5-second speech chunks, whose probabilities are aggregated to produce recording-level decisions with a fixed threshold. On an in-domain clinical cohort of 82 subjects, the proposed method achieved perfect recording-level screening performance (macro-F1 = 1.000, accuracy = 1.000). On a separate out-of-domain set of 131 heterogeneous public Internet recordings, large pretrained speech representations degraded substantially, while MFCC was the strongest baseline (macro-F1 = 0.612, accuracy = 0.641). The proposed method achieved the best out-of-domain performance (macro-F1 = 0.679, accuracy = 0.695), improving on the strongest baseline under the same evaluation protocol. These results suggest that learning a nasality-focused representation before clinical classification can reduce sensitivity to recording artifacts and improve robustness for deployable speech-based VPD screening.

We introduce and study a disorder-free version of the quantum breakdown model with all-to-all interactions. The Hamiltonian factorizes into the product of the zero-momentum-mode occupation number and a quadratic Hamiltonian including only pairing terms. This structure makes the model exactly solvable and produces a large set of zero-energy states. We analyze its spectral, thermodynamic, and dynamical properties. In particular, we show how the factorized structure shapes the spectral form factor and the real-time dynamics. We also compute two-point functions and out-of-time-ordered correlators (OTOCs), and find a distinct early-time growth regime in the OTOCs. These results provide a solvable setting in which spectral properties and real-time dynamics can be analyzed in a controlled way in the absence of disorder, spatial structure, and environmental coupling.

Reliable Sound Source Localization (SSL) plays an essential role in many downstream tasks, where informed decision making depends not only on accurate localization but also on the confidence in each estimate. This need for reliability becomes even more pronounced in challenging conditions, such as reverberant environments and multi-source scenarios. However, existing SSL methods typically provide only point estimates, offering limited or no Uncertainty Quantification (UQ). We leverage the Conformal Prediction (CP) framework and its extensions for controlling general risk functions to develop two complementary UQ approaches for SSL. The first assumes that the number of active sources is known and constructs prediction regions that cover the true source locations. The second addresses the more challenging setting where the source count is unknown, first reliably estimating the number of active sources and then forming corresponding prediction regions. We evaluate the proposed methods on extensive simulations and real-world recordings across varying reverberation levels and source configurations. Results demonstrate reliable finite-sample guarantees and consistent performance for both known and unknown source-count scenarios, highlighting the practical utility of the proposed frameworks for uncertainty-aware SSL.

To address the challenges of high-dimensional channel estimation and underutilized spatial correlations among users in holographic MIMO (HMIMO) systems, this paper proposes a joint graph-cut algorithm for multi-user channel estimation in the wavenumber domain. The size of the conventional angular domain channel matrix increases with the number of antennas in densely-spaced HMIMO. Therefore, user channels are projected into the wavenumber domain via a Fourier harmonic transform, revealing their inherent clustered sparsity and exploiting common scatterer clusters among users. Subsequently, a joint graph-cut channel estimation (JGC-CE) algorithm based on multi-user common supports is designed. In each iteration, the algorithm first partitions user clusters to extract shared supports. Then for each user, it performs users' individual graph update and channel estimation to reconstruct the channel matrix. Simulation results demonstrate that the proposed method outperforms independent estimation schemes for individual users in accuracy while reducing pilot length.

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications.

The optimized stellarator is an attractive concept for which the averaged particle radial drift is zero, and the single particle loss can be significantly reduced. But for the reactor design, global physics such as turbulent transport also need to be optimized besides the confined single particle orbit, or properties estimated using local estimations and heuristic formulations. The first-principle global transport code is too computationally expensive to integrate into the optimization process. The fast surrogate global transport model based on machine learning is a good alternative choice, but the amount of data required to train the surrogate model is numerous due to the high degree-of-freedom of the stellarator design. The work shows that the stellarator design with quasi-helically(QH) symmetric geometry is approximately distributed in a low dimensional latent space, which can be explicitly found by deep learning. This discovery makes it possible to generate global gyrokinetic simulation data for training surrogate models to directly optimize the stellarator geometry for turbulent transport, energetic particle instability, and MHD modes. Using the low dimensional latent space and data analysis methods, the relation between linear zonal residues and axis-excursion is found, providing a simple guide to optimize low turbulent transport QH stellarators.

Optical nanofibers with subwavelength diameters generate strong evanescent fields, enabling efficient light-matter interactions for optical sensing, spectroscopy, and cold-atom experiments. We report a heat-and-pull system for fabricating low-loss optical nanofibers with controllable waist dimensions and investigate the fabrication limits for achieving small waist diameters and long waist lengths. We study factors that influence fabrication performance, including flame geometry, nanofiber dimensions, and surface contamination. Using a multi-hole torch tip that provides a relatively large and uniform heating region, we achieve reproducible fabrication with optical transmission above $99.9\%$ for waist diameters as small as 200 nm for a 1-mm waist length and 250 nm for a 50-mm waist length. We also develop a preparation procedure for fiber splicing and cleaning to minimize transmission loss caused by surface contamination. In addition, we measure long-term transmission degradation due to dust accumulation in a typical laboratory environment and find that nanofibers fabricated in an enclosed setup maintain transmission above $85\%$ for more than 1 hour for nanofibers with a 300-nm waist diameter and waist lengths ranging from 1 to 30 mm. Our work provides practical guidelines for constructing nanofiber fabrication platforms and producing low-loss nanofibers for optics and atomic physics applications.

Addressing Yau's conjecture (Problem 117) on $S^4$, we investigate the self-duality of weakly stable Yang-Mills fields under the assumption of irreducibility. For structure groups with a simple Lie algebra, we prove that any weakly stable irreducible connection must be either self-dual or anti-self-dual. Furthermore, we demonstrate that if the Lie algebra admits a non-trivial abelian center, no irreducible Yang-Mills fields can exist over $S^4$.

Charged lepton flavor violation provides a clear experimental signature in the search for physics beyond the Standard Model. In this work, we study the flavor-violating three-body tau decays $τ^- \to \ell_i^- \ell_i^- \ell_j^+$ ($\ell_i \neq \ell_j$) induced by the scalar leptoquarks $R_2$ and $S_1$, focusing on flavor structures dominated by top- or charm-quark contributions. We compute the one-loop contributions to these processes and derive analytical expressions for the corresponding branching ratios. The phenomenological implications are analyzed for leptoquark masses at the TeV scale, taking into account current constraints from the anomalous magnetic moment of the muon, radiative lepton-flavor-violating decays, and the process $μ^-\to e^-e^-e^+$. Within the allowed parameter space, the predicted branching ratios for $τ^- \to \ell_i^- \ell_i^- \ell_j^+$ can approach the sensitivities expected in near-future experiments. These results highlight the potential of three-body $τ$ decays as probes of lepton-flavor violation and as complementary tests of scalar leptoquark scenarios.

The Nielsen--Ninomiya theorem requires that the total topological chiral charges in a crystal vanish, a constraint typically satisfied by identical nodes like Weyl--Weyl pairs. Whether a minimal heterogeneous configuration -- comprising a single Weyl point (WP) and a single Dirac point (DP) -- can exist in an electronic system has remained unresolved. Here, by systematically classifying all 1651 magnetic space groups (MSGs), we reveal that only 14 MSGs without spin-orbit coupling (SOC) and 10 MSGs with SOC are compatible with this exotic state. Furthermore, for nonmagnetic crystals, this configuration is uniquely realized in the spinless limit of chiral space groups 92 and 96. Guided by this principle, we predict an ideal realization in chiral three-dimensional boron allotropes (SDHBN-B$_{28}$ enantiomers). First-principles calculations unveil a $|C|=2$ WP at the $Γ$ point and a $|C|=2$ DP at the $A$ point, which constitute the only fermions near the Fermi level within a large $2$ eV energy window. Strikingly, the structural chirality rigidly dictates the sign of the topological charges, yielding two ultralong Fermi arcs spanning the surface Brillouin zone. Our work provides a complete crystallographic classification and a definitive material platform for exploring minimal heterogeneous chiral fermions.

We study the one-dimensional isentropic compressible Euler equations with linear (frictional) damping, subject to multiplicative, white-in-time stochastic forcing. The system is posed on a bounded interval with $L^\infty$ initial data and Dirichlet boundary conditions imposed on the momentum. We establish the global-in-time existence of $L^\infty$ martingale solutions that satisfy an appropriate entropy inequality. Then, we analyze the long-time behavior of these solutions and show that, under suitable assumptions on the noise, they converge almost surely and exponentially fast to a constant steady state of the system. The limiting density is well-approximated by the asymptotic solution of the deterministic porous medium equation, while the momentum exhibits the asymptotic behavior predicted by Darcy's law. The analysis in the stochastic setting is delicate, as temporal white-noise perturbations can significantly influence the long-time statistics of the solution. Our approach hinges on deriving sharp moment estimates for the entropy, which enable us to quantify and ultimately prove the decay of stochastic effects. To the best of our knowledge, this work provides the first rigorous pathwise convergence result for the long-time behavior of solutions to the stochastic isentropic compressible Euler equations with linear damping.

Multimodal models often converge to a dominant-modality solution, in which a stronger, faster-converging modality overshadows weaker ones. This modality imbalance causes suboptimal performance. Existing methods attempt to balance different modalities by reweighting gradients or losses. However, they overlook the fact that each modality has finite information capacity. In this work, we propose IIBalance, a multimodal learning framework that aligns the modality contributions with Intrinsic Information Budgets (IIB). We propose a task-grounded estimator of each modality's IIB, transforming its capacity into a global prior over modality contributions. Anchored by the highest-budget modality, we design a prototype-based relative alignment mechanism that corrects semantic drift only when weaker modalities deviate from their budgeted potential, rather than forcing imitation. During inference, we propose a probabilistic gating module that integrates the global budgets with sample-level uncertainty to generate calibrated fusion weights. Experiments on three representative benchmarks demonstrate that IIBalance consistently outperforms state-of-the-art balancing methods and achieves better utilization of complementary modality cues. Our code is available at: https://github.com/XiongZechang/IIBalance.

Motivated by the stringent requirements of the Upstream Pixel (UP) tracker in the LHCb Upgrade II and the Inner Tracking detector (ITK) of the Circular Electron Positron Collider, the COFFEE series of pixel sensor chips have been developed using a 55nm High-Voltage CMOS (HVCMOS) process. The primary objective is to achieve a time resolution of a few nanoseconds under a hit density of up to 100 MHz/cm$^2$, while maintaining fine spatial resolution ($\sim$10 $μ$m) and reasonable power consumption ($<$200 mW/cm$^2$). Building on the process validation of the COFFEE2 prototype, this work presents the design and preliminary test results of COFFEE3-a prototype integrating two distinct readout architectures. Architecture 1, tailored for the current triple-well process, adopts NMOS-only in-pixel circuitry and innovative column-level readout to handle high hit densities. The time walk of pixel-level signal is controlled within 10 ns, and the Time of Arrival (TOA) and Time over Threshold (TOT) are measured with a system clock with the period of 25 ns in peripheral circuits. Architecture 2, developed for future possible processes with p-type buried layer isolation, features pixel-level time measurement and storage. A chip-level Time-to-Digital Converter (TDC) is used and the part of Voltage-Controlled Delay Line (VCDL) is copied in each pixel to get a high time resolution. The TOA resolution is estimated to be 4.2 ns and the TOT resolution 8.4 ns. COFFEE3, with a layout size of 3$\times$4 mm$^2$, was manufactured and has undergone preliminary tests. Charge injection tests for analog circuits, and laser tests for full readout chains, confirm that both architectures operate as expected. Next step work will focus on characterizing key performance such as the timing resolution, radiation hardness, and tracking performance of minimum ionising particles.

This paper studies randomized polynomial kernelization for the weighted $d$-matroid intersection problem. While the problem is known to have a kernel of size $O(d^{(k - 1)d})$ where $k$ is the solution size, the existence of a polynomial kernel is not known, except for the cases when either all the given matroids are partition matroids~(i.e., the $d$-dimensional matching problem) or all the given matroids are linearly representable. The main contribution of this paper is to develop a new kernelization technique for handling general matroids. We first show that the weighted $d$-matroid intersection problem admits a polynomial kernel when one matroid is arbitrary and the other $d-1$ matroids are partition matroids. Interestingly, the obtained kernel has size $\tilde{O}(k^d)$, which matches the optimal bound~(up to logarithmic factors) for the $d$-dimensional matching problem. This approach can be adapted to the case when $d-1$ matroids in the input belong to a more general class of matroids, including graphic, cographic, and transversal matroids. We also show that the problem has a kernel of pseudo-polynomial size when given $d-1$ matroids are laminar. Our technique finds a kernel such that any feasible solution of a given instance can reach a better solution in the kernel, which is sufficiently versatile to allow us to design parameterized streaming algorithms and faster EPTASs.

Chance-constrained optimization is a suitable modeling framework for safety-critical applications where violating constraints is nearly unacceptable. The scenario approach is a popular solution method for these problems, due to its straightforward implementation and ability to preserve problem structure. However, in the rare-event regime where constraint violations must be kept extremely unlikely, the scenario approach becomes computationally infeasible due to the excessively large sample sizes it demands. We address this limitation with a new yet straightforward decision-scaling method that relies exclusively on original data samples and a single scalar hyperparameter that scales the constraints in a way amenable to standard solvers. Our method leverages large deviation principles under mild nonparametric assumptions satisfied by commonly used distribution families in practice. For a broad class of problems satisfying certain practically verifiable structural assumptions, the method achieves a polynomial reduction in sample size requirements compared to the classical scenario approach, while also guaranteeing asymptotic feasibility in the rare-event regime. Numerical experiments spanning finance and engineering applications show that our decision-scaling method significantly expands the scope of problems that can be solved both efficiently and reliably.

We present a bar-informed kinematic-distance (BIKD) method to reconstruct face-on molecular-gas maps of the inner Milky Way from PPV data, relaxing the standard assumption of axisymmetric circular rotation that can generate severe artifacts in barred regions. BIKD replaces the rotation curve with a non-axisymmetric streaming field extracted from hydrodynamical simulations in an observationally constrained barred Galactic potential, and infers a discrete distance posterior along each sightline using a Gaussian likelihood in line-of-sight velocity. To mitigate multi-modality, we adopt posterior-weighted map making via posterior sampling. We validate the full pipeline in closed-loop tests on the simulations, showing that the recovered large-scale morphology is only weakly sensitive to simple distance priors and remains stable across plausible variations in bar angle, snapshot time, and pattern speed. We then apply BIKD to a Galactic CO survey to obtain a face-on $Σ$ map. Compared to a standard axisymmetric kinematic-distance (KD) reconstruction, BIKD strongly suppresses line-of-sight--elongated finger-of-God features and robustly recovers a bar-aligned, quadrant-asymmetric inner-Galaxy morphology under model marginalization. The model-marginalized radial profiles show an approximately exponential decline beyond $\sim4$ kpc, a pronounced deficit at $R\sim0.5$--$3.5$ kpc, and a central concentration consistent with Central Molecular Zone surface densities. Finally, we compare prominent ridge-shaped overdensities in the BIKD map with independent spiral-arm loci traced by high-mass star-forming region masers with VLBI trigonometric parallaxes and by classical Cepheids with period--luminosity distances. Several maser-parallax segments are qualitatively consistent with the dominant BIKD ridges, whereas the Cepheid loci do not coincide with them within their recommended azimuth range.

Urban flood emergency response increasingly relies on infrastructure impact forecasts rather than hazard variables alone. However, real-time predictions are unreliable due to biased rainfall, incomplete flood knowledge, and sparse observations. Conventional open-loop forecasting propagates impacts without adjusting the system state, causing errors during critical decisions. This study presents CRAF (Crowdsourcing-Enhanced Real-Time Awareness and Forecasting), a physics-informed, closed-loop framework that converts sparse human-sensed evidence into rolling, decision-grade impact forecasts. By coupling physics-based simulation learning with crowdsourced observations, CRAF infers system conditions from incomplete data and propagates them forward to produce multi-step, real-time predictions of zone-level building functionality loss without online retraining. This closed-loop design supports continuous state correction and forward prediction under weakly structured data with low-latency operation. Offline evaluation demonstrates stable generalization across diverse storm scenarios. In operational deployment during Typhoon Haikui (2023) in Fuzhou, China, CRAF reduces 1-3 hour-ahead forecast errors by 84-95% relative to fixed rainfall-driven forecasting and by 73-80% relative to updated rainfall-driven forecasting, while limiting computation to 10 minutes per update cycle. These results show that impact-state alignment-rather than hazard refinement alone-is essential for reliable real-time decision support, providing a pathway toward operational digital twins for resilient urban infrastructure systems.

Reference lists in scholarly manuscripts frequently contain errors, including incorrect identifiers, incomplete metadata, misattributed authors, and mismatches between preprint and published versions. These problems are tedious to repair manually and have become more visible in workflows that rely on large language models, which can fabricate or corrupt citations. We present citecheck, a TypeScript system and MCP server for automated bibliographic verification and repair in paper-like project folders. Given a manuscript file or workspace, citecheck selects the most likely paper artifact, extracts references from .bib, .tex, .md, .txt, or .docx, validates entries against PubMed, Crossref, arXiv, and Semantic Scholar, and returns structured correction proposals together with replacement-safety diagnostics. The current repository provides a working research prototype with multi-pass retrieval, manifestation-aware matching, policy-gated rewrite planning, and 47 passing tests covering repair behavior, malformed payload handling, transport failures, and MCP exposure. We position citecheck as infrastructure for agentic scholarly editing and as a practical guardrail against both traditional reference errors and LLM-induced citation hallucinations.

We work with infinite, closed, translation-invariant, finite-range lattice systems with "unbounded classical spins", also known as anharmonic crystals, under assumptions close to those used by Lanford, Lebowitz and Lieb (J. Stat. Phys., 1977); among other conditions, the pinning dominates the interaction. In this context, we prove conservation of the specific energy and specific entropy under the time evolution, and we discuss their relation to approach to thermal equilibrium, paralleling known results in the theory of quantum spin systems, where noncommutativity, as opposed to lack of compactness, is the main source of difficulties.

Upcoming astronomical surveys produce imagery that spans many orders of magnitude in spatial scale, requiring scientists to reason fluidly between global structure and local detail. Data from the Vera C. Rubin Observatory exemplifies this challenge, as traditional desktop-based workflows often rely on discrete views or static cutouts that fragment context during exploration. This paper presents a design-oriented framework for scale-aware navigation of astronomical survey imagery in high-resolution immersive display environments. We illustrate these principles through representative usage scenarios using Vera Rubin Observatory and Milky Way survey imagery deployed in room-scale immersive environments, including tiled high-resolution displays and curved immersive systems. Our goal is to contribute design insights that inform the development of immersive interaction paradigms for exploratory analysis of extreme-scale scientific imagery.

Brain drain -- the emigration of skilled individuals toward higher-wage economies -- is a well-documented phenomenon, yet its aggregate economic cost remains difficult to quantify because individual productivity is rarely observed. We offer a novel angle on this measurement challenge by studying professional football, a global labour market in which every participant carries a publicly observable, consistently estimated market value. Using data on over 92,000 professional footballers worldwide from Transfermarkt, we identify nearly 20,000 players with multi-national eligibility and compute the implied transfer of human capital between countries. We find that the resulting "leg drain" disproportionately benefits wealthy European nations -- France alone gains over EUR3 billion in player value -- while African and Caribbean countries bear the largest losses relative to GDP. Italy is the single largest net loser in absolute terms, driven by the outflow of players with Italian heritage to Latin American national teams. A gravity model of bilateral flows reveals that former colonial ties are among the strongest predictors of leg drain intensity: countries with a colonial relationship to a major European footballing nation lose significantly more player value, even after controlling for population and income. These findings provide a transparent, quantifiable analogue to the broader brain drain debate and highlight how historical institutional links continue to shape global talent redistribution.

We study a target coverage problem in which a team of sensing agents, operating under limited communication, must collaboratively monitor targets that may be adaptively repositioned by an attacker. We model this interaction as a zero-sum game between the sensing team (known as the defender) and the attacker. However, computing an exact Nash equilibrium (NE) for this game is computationally prohibitive as the action space of the defender grows exponentially with the number of sensors and their possible orientations. Exploiting the submodularity property of the game's utility function, we propose a distributed framework that enables agents to self-configure their communication neighborhoods under bandwidth constraints and collaboratively maximize the target coverage. We establish theoretical guarantees showing that the resulting sensing strategies converge to an approximate NE of the game. To our knowledge, this is the first distributed, communication-aware approach that scales effectively for games with combinatorial action spaces while explicitly incorporating communication constraints. To this end, we leverage the distributed bandit-submodular optimization framework and the notion of Value of Coordination that were introduced in [1]. Through simulations, we show that our approach attains near-optimal game value and higher target coverage compared to baselines.

Let $0<γ_1\leq γ_2\leq \ldots$ denote the positive ordinates of the non-trivial zeros of the Riemann zeta-function. A result first announced by Selberg states that there exist absolute constants $Θ, \vartheta>0$ such that for each $r\in \mathbb{N}$, \[ \limsup_{n\to \infty}\frac{γ_{n+r}-γ_n}{2πr/\log γ_n}\geq 1+\fracΘ{r^α} \qquad \text{and}\qquad \liminf_{n\to \infty}\frac{γ_{n+r}-γ_n}{2πr/\log γ_n}\leq 1-\frac{\vartheta}{r^α} \] where $α$ may be taken as $2/3$, or as $1/2$ if one assumes the Riemann hypothesis. This was recently proved by Conrey and Turnage-Butterbaugh under RH and by Inoue unconditionally. We prove that in fact a positive proportion of $r$-gaps are large (and small) to the above extent, and we provide explicit estimates for the sizes and proportions of these gaps. In the case $r=1$, this quantitatively improves an unconditional result of Simonič, Trudgian and Turnage-Butterbaugh.

In this paper, we show combinatorial identities that represent powers of positive integers using multinomial coefficients, which do not come from the multinomial theorem and the multinomial Vandermonde's convolution.

Federated computing (FC) enables collaborative computation such as machine learning, analytics, or data processing across distributed organizations keeping raw data local. Built on four architectural pillars, distributed data assets, federated services, standardized APIs, and decentralized services, FC supports sovereignty-preserving collaboration. However, federated systems spanning organizational and jurisdictional boundaries lack a portable mechanism for enforcing sovereignty-critical constraints. They often depend on runtime policy evaluation, shared trust infrastructure, or institutional agreements that introduce coordination overhead and provide limited cryptographic assurance. Federated Computing as Code (FCaC) is a declarative architecture that addresses this gap by compiling authority and delegation into cryptographically verifiable artifacts rather than relying on online policy interpretation. Boundary admission becomes a local verification step rather than a policy decision service. FCaC separates constitutional governance from procedural governance. Admission is validated locally at execution boundaries using proof-carrying capabilities, while stateful services may still implement post-admission controls such as ABAC, risk scoring, quotas, and workflow state. FCaC introduces Virtual Federated Platforms (VFPs), which combine Core, Business, and Governance contracts through a cryptographic trust chain: Key Your Organization (KYO), Envelope Capability Tokens (ECTs), and proof of possession (PoP). We demonstrate the approach in a proof-of-concept cross-silo federated learning workflow using MNIST as a surrogate workload to validate the admission mechanisms and release an open-source implementation showing envelope issuance, boundary verification, and envelope-triggered training.

Machine Learning (ML) cloud services, offered by leading providers such as Amazon, Google, and Microsoft, enable the integration of ML components into software systems without building models from scratch. However, the rapid adoption of ML services, coupled with the growing complexity of business requirements, has led to widespread misuses, compromising the quality, maintainability, and evolution of ML service-based systems. Though prior research has studied patterns and antipatterns in service-based and ML-based systems separately, automatic detection of ML service misuses remains a challenge. In this paper, we propose MLmisFinder, an automatic approach to detect ML service misuses in software systems, aiming to identify instances of improper use of ML services to help developers properly integrate ML components in ML service-based systems. We propose a metamodel that captures the data needed to detect misuses in ML service-based systems and apply a set of rule-based detection algorithms for seven misuse types. We evaluated MLmisFinder on 107 software systems collected from open-source GitHub repositories and compared it with a state-of-the-art baseline. Our results show that MLmisFinder effectively detects ML service misuses, achieving an average precision of 96.7\% and recall of 97\%, outperforming the state-of-the-art baseline. MLmisFinder also scaled efficiently to detect misuses across 817 ML service-based systems and revealed that such misuses are widespread, especially in areas such as data drift monitoring and schema validation.

The Sen index and Sen-Shorrocks-Thon (SST) index are widely used measures of poverty indices. Developing reliable inference for these measures enables us to compare these measures in different populations of interest in an effective way. It is important to construct confidence intervals for the Sen index and SST index, which provide better coverage probability and shorter interval length. Motivated by this, we discuss empirical likelihood (EL) and jackknife empirical likelihood (JEL) based inference for the Sen index. To derive a JEL-based confidence interval for the Sen and SST indices, we propose a new estimator for the Sen index using the theory of U-statistics and examine its properties. The large sample properties of the EL and JEL ratio statistics are studied. We also discuss EL and JEL-based inference for the Sen-Shorrocks-Thon (SST) index. The finite sample performance of the EL and JEL-based confidence intervals of both Sen and SST indices is evaluated through a Monte Carlo simulation study. Finally, we illustrate our methods using individual-level data from the Panel Study of Income Dynamics (PSID) survey from the US as well as Indian household level income data for different states sourced from the Consumer Pyramids Household Survey (CPHS).

We revisit and sharpen a recent observable regularity criterion for the three-dimensional Navier-Stokes equations on the periodic cube by requiring only finitely many measurements of the flow on a given time interval. Two data models are treated: (i) modal observations (a finite set of low Fourier modes), and (ii) nodal observations, i.e. values of the velocity field sampled at finitely many points on a uniform grid. The key upgrade is a piecewise linear interpolant built on a fixed five-tetrahedra subdivision of each grid cube, which removes the mollification step used previously and yields an explicit control of the derivatives of the interpolation operator purely in terms of the measured data. The criterion is also shown to be both necessary and sufficient for regularity.

The present article is written in the wake of a recently published study of the relation between the light curves of Long Period Variables and their evolution along the Asymptotic Giant Branch (AGB). It introduces a pair of new parameters that describe the shape of the ascending branches of such light curves. This parameterization reveals strong correlations with other parameters describing the evolution of the star on the AGB: periods, regularity and amplitudes of the oscillations, effective temperatures, mass loss rates, carbon and oxygen isotopic ratios, colour indices. It sheds new light on the occasional presence of humps on the ascending branches and on the distinction between stars that have not experienced strong Third Dredge Up (TDU) events and those that have as well as between oxygen-rich and carbon-rich stars. One of the new parameters, referred to as q, is particularly efficient at tracking the evolution of the star along the AGB. Globally, a simple picture can be drawn in the plane spanned by the period and by q. Yet, many details remain unexplained when looking at this picture in finer details. Overall, the results presented in the article help significantly with clarifying the complex set of observations that have been made in this domain and should inspire new considerations on their relation with the underlying physical mechanisms at stake inside the stars.