Indoor wireless communication environments are strongly influenced by dynamic conditions, which affect channel state information (CSI) and, consequently, the precoding strategy and the selection of the access point (AP). Device-free sensing and localization functionalities can provide information about these conditions, including, for example, the user's position and the position of mobile blocking objects. To model the statistical relationship between the CSI and the provided conditions, we employ a conditional variational autoencoder (cVAE). We treat the user and object positions - referred to as context information - as conditional inputs to the cVAE. The proposed model does not rely on ground-truth CSI and is trained directly on noisy data. Once trained, the framework can infer channel statistics solely from user and blocking object positions, enabling proactive AP selection based on inferred statistical CSI without requiring continuous CSI estimation. Extensive simulations with the state-of-the-art ray-tracing tool Sionna validate the proposed method.

Self-sustained neural activity in the absence of ongoing external input is a fundamental feature of nervous system dynamics, yet the conditions under which it can emerge in biophysically grounded network models remain incompletely understood. We studied whether a recurrent network of Hodgkin-Huxley neurons with spike-timing-dependent plasticity and intrinsic stochasticity can maintain autonomous activity after brief transient stimulation. The simulated network comprised 200 neurons (160 excitatory, 40 inhibitory) with 80% connection probability, incorporating excitatory and inhibitory STDP, probabilistic vesicle release, probabilistic synapse formation, receptor variability, and voltage-dependent inhibition. After a brief 200 ms initialization stimulus to 30 excitatory neurons, the network received no further external input. In one 1800 s simulation and two additional 500 s simulations, the network maintained sparse, irregular activity without ongoing drive. In the 1800 s run, 67% of neurons exhibited mean firing rates below 1 Hz, the population mean firing rate was 1.13 +/- 1.34 Hz, participation increased across longer observation windows, and population-mean Fano factors remained near 1-2, consistent with irregular spike timing. Raster activity also showed spontaneous qualitative reorganizations in collective firing patterns over time. These findings suggest that recurrent Hodgkin-Huxley networks with plastic and stochastic synapses can sustain long-duration autonomous activity in a sparse firing regime after brief initialization.

We extend the investigation into the formation of shell-crossing singularites (SCS) in effective polymerized LTB models to the LQG-inspired asymmetric bounce model, as well as to effective LTB models based on the solutions of Bardeen and Hayward, in which no bounce occurs. While the asymmetric bouncing model belongs to the class of bounded polymerization functions, the latter models feature unbounded polymerization functions. Our results show that, similar to the symmetric bouncing model, for the asymmetric bouncing model SCS are unavoidable for inhomogeneous dust profiles. In contrast, for models without a bounce and with unbounded polymerization functions, no SCS form for inhomogeneous, decreasing dust profiles -- a situation that resembles classical theory, in which SCS can also be avoided by a suitable choice of initial data.

Accurate building load forecasting plays a critical role in facilitating demand response aggregation and optimizing energy management. However, the complex temporal dependencies and high volatility of building loads limit the improvement of prediction accuracy. To this end, we propose a novel end-to-end building load forecasting framework. Specifically, the framework can be divided into two main stages. In the two-stage data preprocessing module enhanced by interpretable feature selection, we utilize the Local Outlier Factor (LOF) algorithm to accurately detect and correct anomalies in the original building load series. Furthermore, we employ SVM-SHAP feature analysis to quantify the impact of environmental variables, filtering out critical feature combinations to mitigate redundancy. In the building load forecasting module, we propose the patch-based information fusion network (PIF-Net). This model applies patching technology to process input series into local blocks, extracting temporal features through a shared Gated Recurrent Unit (GRU) network with residual connections. Subsequently, an information fusion module based on a customized gating mechanism integrates the ensemble hidden states to weight the importance of different temporal patches dynamically. Additionally, the framework is trained using a novel Error-weighted Adaptive Loss (EWAL) function. By combining a rational quadratic function and logarithmic loss to dynamically adjust penalty weights based on real-time prediction error distributions, EWAL significantly enhances the model's robustness under extreme load conditions. Finally, extensive experiments demonstrate the effectiveness and superiority of our proposed framework.

Planning empirical experiments such as clinical trials or A/B tests requires sample size determination, which in many interesting cases has no closed-form solution (e.g. factorial or adaptive designs). adsasi is a new R package that enables simulations-first sample size calculations for any trial that can be simulated in short compute time. First, the user specifies as a function that takes a sample size as argument, simulates the experiment, and returns a boolean for success/failure. Then, adsasi functions adsasi_0d and adsasi_1d iteratively call it on different sample sizes and progressively home in on the one with nominal success rate (power), assuming that increasing sample size increases power. adsasi_1d can also draw, purely empirically, the relationship between a design parameter and sample size. The implementation uses a modified probit regression (with success/failure as the dependent variable), informed by simulations conducted around the target size, and provides standard errors at each stage using the Cramér-Rao bound derived from a custom analytical Hessian matrix. Simple examples are first presented, yielding results within Monte Carlo variance of their closed-form expressions, then intractable ones (including bootstrapping from an existing medical cohort). adsasi will hopefully facilitate the funding and conduct of interesting, highly complex experimental designs by making their sizing straightforward.

We address the multi-agent motion planning problem where interactions, collisions, and congestion co-exist. Conventional game-theoretic planners capture interactions among agents but often converge to conservative, congested equilibria. Homotopy planners, on the other hand, can explore topologically distinct paths, but lack mechanisms to account for the interdependence of agents' future actions. We propose a unified framework that leverages homotopy classes as structured strategy sets within a receding-horizon setup. At each planning stage, a deterministic homotopy planner generates topologically distinct paths for each agent, conditioned on the joint configuration. To avoid intractable growth of candidate paths, we propose a simple heuristic filtering step that selects a top-$K$ subset of the most suitable congestion-free joint strategies to ensure computational tractability. These serve as initializations for a potential game that enforces homotopy-consistent constraints and yields a generalized open-loop Nash equilibrium (OLNE), with penalties discouraging abrupt strategy shifts in a receding-horizon setting. Simulations with three agents demonstrate improved efficiency (faster completion) and enhanced safety (greater inter-agent clearance, leading to reduced congestion) compared to a local baseline and NH-ORCA that do not reason about homotopies. Hardware trials with two robots and one human demonstrate robustness to irrational behaviors, where our method adapts by switching to alternative feasible equilibria while the baseline game fails.

In process operations, it is desirable to manage the sensitivity of the system output against external disturbance in the form of finite $\mathcal{L}_2$-gain stabilization. This matter is, however, nonsensical for stochastic systems because the stochastic uncertainties in the control input almost always lead to an unbounded $\mathcal{L}_2$ gain from the disturbance to the output. To address this issue, this article develops a novel concept that characterizes the $\mathcal{L}_2$ gain of stochastic systems in a probabilistic way. Combined with a large data set, we formulate a data-driven probabilistic finite $\mathcal{L}_2$-gain stabilization design using noisy trajectory measurements and the disturbance forecast that does not necessarily agree with the actual future disturbance. The design approach consists of a data-driven trajectory estimation algorithm, whose resulting estimation error covariance is nicely integrated into the feasibility conditions for controller synthesis, leading to a convex offline design in the form of linear matrix inequalities. The effectiveness of the proposed design, along with the additional insights provided by the approach, is illustrated via a numerical example.

Accurately modelling many-body quantum transport systems poses a challenge both conceptually and computationally due to the growth of the Hilbert space and the multi-scale nature of the geometries and couplings present in most naturally occurring networks. A compounding complexity of such systems is that the environment typically plays a key role in the transport dynamics. Utilising variational unitary transformations that displace environmental degrees of freedom allows for the deployment of a second-order master equation capable of capturing the dynamics of intermediate and strongly coupled systems, which are ubiquitous in microscopic energy transport systems. However, direct implementations of this approach suffer from fundamental scalability issues due to the complexity of the self-consistent equations required to solve for the variational parameters. Here, we present an efficient partitioning scheme that leverages the inherent multi-scale nature of natural energy transport networks. This enables scaling of the variational polaron framework to quantum energy transport systems, constituting hundreds to thousands of sites. Our work unlocks the physically motivated exploration of large transport networks, for example, those present within light-harvesting complexes and exciton transport in disordered semiconductors.

The pointwise space-time behavior of the Green's function of the three-dimensional modified Vlasov-Poisson-Boltzmann system is studied in this paper. It is shown that the Green's function has a decomposition of the macroscopic diffusive waves and Huygens waves with the speed $\sqrt{\frac{8}{3}}$ at low-frequency, the singular kinetic wave and the remainder term decaying exponentially in space and time. In addition, we establish the pointwise space-time estimate of the global solution to the nonlinear modified Vlasov-Poisson-Boltzmann system based on the Green's function.

We improve the best known upper bounds on the number of Ruelle resonances in disks of large radius for Gevrey uniformly hyperbolic flows. The proof is based on Rugh's approach of dynamical determinants that replaces the study of the flow itself by the analysis of a system of open hyperbolic maps.

The study of spin polarization of $Λ$ hyperons in ultrarelativistic heavy-ion collisions provides insights into the angular momentum and vortical structure of the possible existence of QGP. The present study examines the global spin polarization of $Λ$ hyperons using a second-order relativistic viscous hydrodynamic framework that incorporates medium vorticity, shear viscosity, and evolving magnetic fields. It explores thermal vorticity evolution in relativistic heavy-ion collisions and evaluates its value at the decoupling isothermal freeze-out surface. We quantify the contributions of thermal vorticity and magnetic field to the global spin polarization of $Λ$ hyperons. Comparing results with recent ALICE measurements in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 2.76 and 5.02 TeV shows qualitative agreement, offering new insights into the vortical structure of QCD matter. It also explores the relationship between magnetic and rotational dynamics, with implications for spin polarization at RHIC and LHC energies.

We introduce a notion of global dimension for a triangulated category relative to a compact silting object. We prove that the finiteness of this dimension is an intrinsic property of the triangulated category itself and, therefore, independent of the choice of the silting object. Focusing on the setup of connective differential graded (dg) algebras, we analyse the behaviour of global dimension under dg algebra homomorphisms and establish explicit bounds. This allows us to deduce a bound for the global dimension of certain dg quiver algebras. We also relate the regularity of the big singularity category of a proper connective dg algebra to the finiteness of its global dimension.

The Heisenberg position-momentum uncertainty relation is a cornerstone of quantum mechanics. However, its standard formulation is not fully consistent with special relativity. While partial understanding has been achieved in the ultra-relativistic regime, a comprehensive description is still lacking, particularly in the intermediate velocity domain, where particle speeds remain well below the speed of light yet relativistic corrections are expected to become appreciable. This regime constitutes the most promising arena for experimentally probing relativistic modifications of quantum uncertainty. By adopting a variational approach, in this work we derive a relativistic extension of the Heisenberg algebra within the framework of $κ$-deformed Kaniadakis statistics. The latter emerges from the application of the Maximum Entropy Principle to Kaniadakis entropy, a one-parameter generalization of the Boltzmann-Gibbs-Shannon entropy naturally induced by Lorentz transformations. We investigate the physical implications of the resulting uncertainty relation, deriving constraints on the Kaniadakis parameter from precision measurements of the fine-structure constant and confronting our construction with other extensions discussed in the recent literature.

Neutrinoless double beta decay is a hypothetical nuclear transition whose observation would demonstrate that neutrinos are their own antiparticles and that lepton number is not conserved, with far-reaching implications for the origin of neutrino mass and the matter-antimatter imbalance in the Universe. This review examines the theoretical foundations of this process and surveys the principal experimental strategies developed to search for it, focusing on their operating concepts, strengths, and limitations. We summarize the current experimental landscape by presenting the most sensitive results achieved so far and by outlining the complementary approaches pursued by different detection techniques. Finally, we discuss the future direction of the field, emphasizing the technological advances needed to reach substantially better sensitivities and, ultimately, to detect this rare phenomenon

Performance debugging in WebAssembly (Wasm) runtimes is essential for ensuring the robustness of Wasm, especially since performance issues have frequently occurred in Wasm runtimes, which can significantly degrade the capabilities of hosted services. Many performance issues in Wasm runtimes result from suboptimal compilation of input Wasm programs, for which existing performance debugging methods primarily designed for application-level inefficiencies are not well-suited. In this paper, we present WarpL, a novel mutation-based approach that aims to identify the exact suboptimal instruction sequences responsible for the performance issues in Wasm runtimes, thereby narrowing down the root causes. Specifically, WarpL obtains a functionally similar mutant in which the performance issue does not manifest, and isolates the exact suboptimal instructions by comparing the machine code of the original and mutated programs. We implement WarpL as an open-source tool and evaluate it on 12 real-world performance issues across three widely used Wasm runtimes. WarpL identified the exact causes in 10 out of 12 issues. Notably, we have used WarpL to successfully diagnose six previously unknown performance issues in Wasmtime.

To address the high mobility impacts and the ultra-reliable and low-latency communication (URLLC) requirements in autonomous driving scenarios, rate-splitting multiple access (RSMA) combined with short-packet communication (SPC) emerges as a promising solution.Autonomous vehicles rely on real-time information exchange to ensure safety and coordination, making information freshness essential.By jointly capturing transmission delays and packet errors, age of information (AoI) serves as a comprehensive metric for freshness.In this paper, we investigate short-packet rate splitting to enhance information freshness measured by the AoI.By splitting the unicast messages into common and private parts, encoding all common parts together with the multicast message into a common stream, and encoding each private part into a private stream, RSMA effectively manages interference and enables achieving lower AoI.By considering critical factors such as transmit power, vehicle velocity, blocklength, and the number of transmit antennas, we derive closed-form expressions for the average AoI (AAoI) of the common stream under partial decoding and the overall AAoI under complete decoding.To enhance the AAoI performance, we propose the multi-start two-step successive convex approximation (SCA) algorithm.This algorithm first optimizes the power allocation and subsequently optimizes the rate splitting under the quality of service (QoS) trade-off constraint.Simulation results demonstrate that our short-packet rate-splitting scheme significantly improves the AAoI performance while ensuring system fairness and enabling ultra-low AAoI through the common stream, meeting the requirements of autonomous driving applications.Moreover, the trade-off between the common and overall performance is revealed, indicating that the overall performance can be further enhanced while maintaining the advantages of the common stream.

In a mosaic, a tessera is a single stone. We introduce tesserae for the co-simulation framework mosaik, where they are sets of entities. They allow for a visual, intuitive, and yet systematic description of simulation scenarios by allowing their entities to be created together and the entities of two tesserae to be connected simultaneously, while ensuring that multidirectional data-flow between tesserae remains consistent without further manual synchronization. We further present an extension of mosaik by a graphical user interface (GUI) based on these tesserae, enabling the drag-and-drop creation of co-simulation setups and their execution. The GUI aims to make mosaik more accessible to users previously excluded by its script-based nature. At the same time, it preserves mosaik's flexibility, extensibility, and modular architecture.

This article introduces new methods for the analysis of cyclostationary time series with infinite variance. Traditional cyclostationary analysis, based on periodically correlated (PC) processes, relies on the autocovariance function (ACVF). However, the ACVF is not suitable for data exhibiting a heavy-tailed distribution, particularly with infinite variance. Thus, we propose a novel framework for the analysis of cyclostationary time series with heavy-tailed distribution, utilizing the fractional lower-order covariance (FLOC) as an alternative to covariance. This leads to the introduction of two new autodependence measures: the periodic fractional lower-order autocorrelation function (peFLOACF) and the periodic fractional lower-order partial autocorrelation function (peFLOPACF). These measures generalize the classical periodic autocorrelation function (peACF) and periodic partial autocorrelation function (pePACF), offering robust tools for analyzing infinite-variance processes. Two practical applications of the proposed measures are explored: a portmanteau test for testing dependence in cyclostationary series and a method for order identification in periodic autoregressive (PAR) and periodic moving average (PMA) models with infinite variance. Both applications demonstrate the potential of new tools, with simulations validating their efficiency. The methodology is further illustrated through the analysis of real-world air pollution data, which showcases its practical utility. The results indicate that the proposed measures based on FLOC provide reliable and efficient techniques for analyzing cyclostationary processes with heavy-tailed distributions.

We investigate the consistency and precision of approximate analytical expressions for the electromagnetic self-force acting on a charged particle in Schwarzschild spacetime endowed with weak electromagnetic fields. A fundamental requirement of relativistic particle dynamics is the preservation of the four-velocity normalization ($u^μu_μ=-1$), which implies that the total self-force must remain orthogonal to the particle's four-velocity. We introduce a covariant diagnostic based on the orthogonality condition ($u_μF^μ_{\text{tail}}=0$), which provides a quantitative measure of the internal consistency of approximate tail-term models used in radiation-reaction calculations. We apply this diagnostic to two widely used analytical approximations for the electromagnetic tail force: the conservative component derived by Smith and Will and the dissipative component derived by Gal'tsov. The analysis is performed for several physical configurations, including pure Schwarzschild spacetime, a weakly electrically charged Schwarzschild black hole, and a Schwarzschild black hole immersed in a weak external magnetic field. We find that the conservative Smith--Will term alone leads to small but measurable deviations from the orthogonality condition, while inclusion of the dissipative Gal'tsov contribution suppresses these deviations by many orders of magnitude. For realistic radiation-reaction parameters, the violation becomes extremely small. The proposed orthogonality diagnostic offers a simple and covariant tool for validating approximate self-force models in curved spacetime and may be useful for future studies of radiation-reaction dynamics near compact objects.

The verification of concurrent programs under weak-memory models is a burgeoning effort, owing to the increasing adoption of weak memory in concurrent software and hardware. Release/Acquire has become the standard model for high-performance concurrent programming, adopted by common mainstream languages and computer architectures. In a surprising result, Abdulla et al. (PLDI'19) proved that reachability in this model is undecidable when programs have access to atomic Read-Modify-Write (RMW) operations. Moreover, undecidability holds even for executions with just 4 contexts, and is thus immune to underapproximations based on bounded context switching. The canonical, RMW-free case was left as a challenging question, proving a non-primitive recursive lower bound as a first step, and has remained open for the past seven years. In this paper, we settle the above open question by proving that reachability is undecidable even in the RMW-free fragment of Release/Acquire, thereby characterizing the simplest set of communication primitives that gives rise to undecidability. Moreover, we prove that bounding both the number of context switches and the number of RMWs recovers decidability, thus fully characterizing the boundary of decidability along the dimensions of RMW-bounding and context-bounding.

In large-scale reconfigurable intelligent surface (RIS) communication systems, the precise acquisition of channel state information (CSI) is challenging. Consider a practical RIS configuration where only a few reflective elements serve as anchors to estimate CSI, which are termed partial CSI. To improve the robustness against partial CSI and the scalability of RIS networks, this paper proposes an unsupervised learning-based rate-splitting multiple access (RSMA) scheme for RIS-assisted multi-user systems. Specifically, RISnet, a neural network architecture designed to infer full CSI from partial observations, is employed and integrated with a low-complexity RSMA precoder. Effective channel features are constituted from partial CSI, and the original elements with channel information contribute to new anchors after expansion in RISnet. Numerical results demonstrate that the proposed scheme approximates the performance with a full CSI of RIS under deterministic raytracing channel conditions. When channel uncertainty increases during training, RSMA has been shown to enhance RISnet robustness, significantly mitigating performance loss.

This paper explores the problem of generalized phase retrieval, which involves reconstructing a length-$n$ signal $\bm{x}$ from its $m$ phaseless samples $y_k = \left|\langle \bm{a}_k,\bm{x}\rangle\right|^2$, where $k = 1,2,...,m$, and $\bm{a}_k$ are the measurement vectors. This problem can be reformulated into recovering a positive semidefinite rank-$1$ matrix $\bm{X}=\bm{x}\bm{x}^*$ from linear samples $\bm{y}=\mathcal{A}(\bm{X})\in\mathbb{R}^m$, thereby requiring us to find a rank-$1$ solution of the linear equations. We demonstrate that several existing phase retrieval algorithms, including Wirtinger Flow (WF) and the canonical Riemannian gradient descent (RGD), actually solve the least-squares fitting of this linear equation on the Riemannian manifold of rank-$1$ matrices, but utilize different metrics on this manifold. Nevertheless, these metrics only allow for a stable and far-apart-from-isometric embedding of rank-$1$ matrices to $\mathbb{R}^m$ by $\mathcal{A}$, resulting in a linear convergence with a considerably large convergence factor. To expedite the convergence, we establish a new metric on the rank-$1$ matrix manifold that facilitates the nearly isometric embedding of rank-$1$ matrices into $\mathbb{R}^m$ through $\mathcal{A}$. A RGD algorithm under this new metric, termed Weighted RGD (WRGD), is proposed to tackle the phase retrieval problem. Owing to the near isometry, we prove that our WRGD algorithm, initialized by spectral methods, can linearly converge to the underlying signal $\bm{x}$ with a small convergence factor. Empirical experiments strongly validate the efficiency and resilience of our algorithms compared to the truncated Wirtinger Flow (TWF) algorithm and the canonical RGD algorithm.

We investigate the orientation dynamics of a settling spheroid in simple shear flow, combining a deterministic dynamical-systems analysis with a stochastic Fokker-Planck treatment. The dynamics is governed by the competition between the Jeffery torque from the background shear and the inertial torque from settling. For configurations in which gravity lies in the shear plane, the azimuthal dynamics reduces to overdamped motion in a tilted periodic potential controlled by a single effective parameter $\mathcal{R}$ that combines the particle shape anisotropy and the settling strength. A saddle-node bifurcation on an invariant circle (SNIC) at $\mathcal{R}=1$ governs the transition from sustained rotational motion to steady equilibrium, with the rotation period diverging as $(1-\mathcal{R})^{-1/2}$. When gravity is parallel to the vorticity axis, the attractor is a periodic orbit for all settling strengths. The stochastic analysis reveals that noise plays a fundamentally different role depending on whether settling-induced potential barriers are present: in the classical Jeffery problem it diffuses over the orbit constant, whereas with settling it drives Kramers-type phase slips whose rate is exponentially sensitive to the Péclet number, defined as the ratio of diffusive to convective time scales. Langevin simulations confirm the predicted intermittent dynamics, with phase slips becoming progressively rarer as the barrier height or Péclet number increases. Asymptotic results in both the small- and large-$\mathrm{Pe}$ limits, together with numerical solutions of the Fokker-Planck equation at arbitrary $\mathrm{Pe}$, quantify the orientation moments across all regimes.

This paper studies obfuscation techniques for Erlang programs at the source, abstract syntax tree, BEAM assembly, and BEAM bytecode levels. We focus on transformations that complicate reverse engineering, decompilation, and recompilation while remaining grounded in the actual behavior of the Erlang compiler, validator, loader, and virtual machine. The paper categorizes opcode-level dependency tricks, receive-based loop encodings, irregular control-flow constructions, mutability-oriented performance obfuscation, and self-modifying code enabled by dynamic module loading. A recurring theme is that effective obfuscation in BEAM often arises not from arbitrary corruption, but from exploiting representational gaps between high-level Erlang semantics and the lower-level execution model accepted by the toolchain and runtime.

We derive a one-loop effective description of axion inflation by integrating out a heavy Dirac fermion with an inflaton-dependent complex mass undergoing a smooth localized threshold transition. The threshold induces correlated corrections to the inflaton and gauge sectors, including a Coleman-Weinberg term, a vacuum-polarization correction, and an anomaly-induced Chern-Simons coupling. Together, these effects transiently enhance and localize gauge-field production, generating a chiral stochastic gravitational-wave background in the deci-hertz band within the projected sensitivities of BBO and DECIGO, while remaining below representative primordial-black-hole bounds.

The abundance of process operating data in modern industries, along with the rapid advancement of learning techniques, has led to a paradigm shift towards data-centric analysis and control. However, integrating machine learning with control theory for big data-driven control of nonlinear systems remains a challenging open problem. This is because the state-based, model-centric, and causal framework of classical control theory fundamentally contradicts the trajectory-based, set-theoretic, and causality-absent rationale of big data-based learning approaches. Using the behavioral framework, we show that dynamical systems possess an intrinsic state variable that encodes the system behavior in a bijective and causality-free manner, and control design can be carried out entirely within the state space. This approach not only resolves the aforementioned conflict but also complements machine learning techniques well, leading to a neural network architecture that is capable of learning the behavior representation well-suited for control design.

During the first superior conjunction of the Tianwen-1 Mars probe in October 2021, its downlink signal received by the Wuqing 70-m radio telescope passed within 4.53 solar radii of the Sun. The signal was significantly perturbed by the solar wind, providing a mechanism to probe coronal activity. We analyze the Doppler frequency scintillation spectrum of the solar wind within 10 solar radii to derive a characteristic frequency scintillation parameter. Statistical analysis indicates this parameter increases as the signal path approaches the Sun, with notable anomalies observed on October 5, 13, and 15. Comparisons with SOHO and SDO data reveal strong spatio-temporal correlations between these scintillation anomalies and coronal activity. We demonstrate that this parameter effectively identifies solar phenomena, including coronal streamers, high-speed solar wind, and coronal mass ejections (CMEs). Quantitative analysis confirms a distinct temporal correlation and delay between frequency scintillation and solar wind speed changes, validating the feasibility of spatially localizing solar activity.

In this work we study the large-time behaviour of solutions of the Heat Equation in the hyperbolic space $\mathbb{H}^d$, providing precise speeds of convergence in $L^1$ and $L^\infty$ to their asymptotic profiles by means of an adaptation of entropy estimates. For $L^1$ initial conditions we are able to identify the asymptotic profile in $L^1$, which is not universal but contains a certain memory of the initial distribution of the mass of the solution. We improve thus on previous results, where speed of convergence was absent and asymptotic profiles where not known in the general case, and show a way to adapt entropy estimates employed in the study of diffusion processes to non-compact Riemannian manifolds. The main strategy to prove this is to consider transient profiles as minimizers of the entropy functional. These profiles are time-dependent and encompass the geometric information of the Riemannian manifold.

European digital sovereignty and security increasingly depends on whether high-impact decisions can be grounded in location evidence that remains credible under adversarial pressure. This paper frames a cross-sector analysis as a location-data provenance problem: not merely what a device or service reports as location, but whether there is contestable evidence about where and when an asserted event occurred, who or what produced the assertion, and under which audit and retention guarantees. There are observable patterns across democratic processes and the information environment, trade and origin-sensitive supply chains, finance and illicit shipping flows, critical infrastructure and mobility, and harms targeting individuals' private and social domains. In these patterns we see a recurring asymmetry in which locality, presence, routing, or jurisdiction can be asserted cheaply while institutions and affected parties face costly reconstruction when disputes arise. To make this challenge actionable, this paper introduces a compact risk taxonomy that decomposes provenance failures into integrity axes and recurring failure modes, and derives design expectations for next-generation digital trust infrastructure centered on contestability under dispute, while remaining privacy- and rights-compatible. It argues for treating location as a digital primitive that should be represented as evidence-bearing claims rather than self-asserted coordinates, and positions proof-of-location (PoL) mechanisms as a candidate capability layer for producing verifiable presence claims under explicit threat and privacy assumptions. The outcome is a sector-neutral foundation for future architectural work on a next-generation digital trust infrastructure for Europe.

A good number of toolkits have been developed in Recommender Systems (RecSys) research to promote fair evaluation and reproducibility. However, recent critical examinations of RecSys evaluation protocols have raised concerns regarding the validity of existing evaluation pipelines. In this demonstration, we present RecNextEval, a reference implementation of an evaluation framework specifically designed for next-batch recommendation. RecNextEval utilizes a time-window data split to ensure models are evaluated along a global timeline, effectively minimizing data leakage. Our implementation highlights the inherent complexities of RecSys evaluation and encourages a shift toward model development that more accurately simulates production environments. The RecNextEval library and its accompanying GUI interface are open-source and publicly accessible.