We study the worst-case approximation of multivariate periodic functions from the weighted Korobov space $H_{d,α,γ}$ with smoothness $α>1/2$ in the Lebesgue norm $L_p([0,1]^d)$ for $1\le p\le\infty$. We analyze a \emph{median lattice algorithm} that reconstructs a truncated Fourier series by approximating the coefficients on a hyperbolic-cross-type index set using $R$ rank-1 lattice sampling rules with independent randomly chosen generating vectors, and then aggregating the resulting coefficient estimators via the componentwise median. For an odd number of repetitions $R>1$ and an odd prime lattice size $N$, we prove high-probability error bounds in both $L_\infty$ and $L_2$. Interpolation then yields the result for all $1 \le p\le\infty$. In particular, with a high probability, the algorithm satisfies \[ \mathrm{err}(H_{d,α,γ},L_p,A)\ \le\ C_{d,α,β,\boldsymbolγ,p}\, N^{- α+ (\frac12 - \frac1p)_+ + β}, \qquad 1 \le p\le\infty,\ β>0, \] where $(x)_+ = \max\{x, 0\}$, $N$ is the number of function evaluations, and the weights $\boldsymbolγ$ and the constant $C_{d,α,β,\boldsymbolγ,p}$ are independent of $N$. For $p=\infty$, $C_{d,α,β,\boldsymbolγ,\infty}$ is dimension-independent under the summability condition $\sum_{j=1}^\infty γ_j^{1/(2α)}<\infty$. These results extend recent analyses of median-based lattice approximation in $L_2$ and complement related multiple-shift lattice approaches, showing that median aggregation yields nearly optimal $L_p$-approximation rates (up to logarithmic factors and an arbitrarily small loss) in weighted Korobov spaces.

Consider the random subgraph process on a base graph $G$ with $n$ vertices: we generate a sequence $\{G_t\}_{t=0}^{|E(G)|}$ by taking a uniformly random ordering of the edges of $G$ and then adding these edges one by one to the empty graph $G_0$ on the same vertex set. We prove that there is a constant $C > 0$ such that if $G$ is an $(n,d,λ)$-graph with $d/λ\ge C$, then with high probability, the hitting time for the appearance of a Hamilton cycle coincides with the hitting time for reaching minimum degree $2$. This resolves questions posed by Alon--Krivelevich in 2019 and by Frieze--Krivelevich in 2002. As a consequence, we determine the sharp threshold for Hamilton cycles in $(n,d,λ)$-graphs with $d/λ\ge C$ for all $d$ sufficiently large. Lastly, we extend our result to the minimum degree $2k$ versus $k$ edge-disjoint Hamilton cycles setting for $k \leq c\cdot \min\{d,\log n\}$ where $c$ is a constant depending on $C$. This advances on a question asked by Frieze.

While it is well known that galaxies are composites of many emission processes, quantifying the various contributions remains challenging. In this work, we use unsupervised machine learning based clustering algorithms to evaluate the agreement between the clustering tools and astrophysical classifications, and hence quantify the fractional contributions of star formation processes and nuclear black hole activity to the total galaxy energy budget of radio sources. We perform clustering on the multiwavelength (optical, infrared (IR), and radio) active galactic nuclei (AGN) diagnostic spaces, using the data from the G09 and G23 fields from the Galaxy and Mass Assembly (GAMA) survey, Evolutionary Map of the Universe (EMU) survey, and the Wide-field Infrared Survey Explorer (WISE). We find that the statistical clustering recovers $\approx$ 90 % of the star forming galaxies (SFGs) and $\approx$ 80 % of the AGN. We define a new IR-radio AGN diagnostic scheme that identifies radio AGN from IR SFGs and AGN, corresponding to the KMeans cluster with approximately 90 % reliability. We demonstrate the superior power of radio AGN selection in higher dimensions using a three-dimensional space composed of directly observable parameters ($\rm W_1-W_2$ colour, $\rm W_2$ magnitude, and the 1.4 GHz radio flux density). This novel three dimensional diagnostic shows immense potential in radio AGN selection that is close to 90 % reliable and 90 % complete. We also publish a catalogue of radio sources in the EMU survey with associated probabilities for them to be active in the optical regime, through which we emphasise the philosophy of considering a galaxy to be composed of various fractions rather than a binary classification of SFGs and AGN.

Randomized response is a popular local anonymization approach that can deliver anonymized multi-dimensional data sets with rigorous privacy guarantees. At the same time, it can ensure validity for exploratory analysis and machine learning tasks as, under fairly general conditions, unbiased estimates of the underlying true distributions can be retrieved. However, and like for many other anonymization techniques, one of the main pitfalls of this approach is the curse of dimensionality. When coping with data sets with many attributes, one quickly runs into unsustainable computational costs for estimating true distributions, as well as a degradation in their accuracies. Relying on new theoretical insights developed in this paper, we propose an approach to multi-dimensional randomized response that avoids these traditional limitations. From simple yet intuitive parameterizations of the randomization matrices that we introduce, we develop a protocol called Lambda-randomization that entails low computational costs to retrieve estimates of multivariate distributions, and that makes use of solely three simple elements: a set of parameters ranging between 0 and 1 (one per attribute of the data set), the identity matrix, and the all-ones vector. We also present an empirical application to illustrate the proposed protocol.

Extreme values and the tail behavior of probability distributions are essential for quantifying and mitigating risk in complex systems of all kinds. In multivariate settings, accounting for correlations is crucial. Although extreme value analysis for infinite correlated systems remains an open challenge, we propose a practical framework for handling a large but finite number of correlated time series. We develop our approach for finance as a concrete example but emphasize its generality. We study the extremal behavior of high-frequency stock returns after rotating them into the eigenbasis of the correlation matrix. This separates and extracts various collective effects, including information on the correlated market as a whole and on correlated sectoral behavior from idiosyncratic features, while allowing us to use univariate tools of extreme value analysis. This holds even for high-frequency data where discretization effects normally complicate analysis. We employ a peaks-over-threshold approach and thereby fully avoid the analysis of block maxima. We estimate the tail shape of the rotated returns while explicitly accounting for nonstationarity, a key feature in finance and many other complex systems. Our framework facilitates tail risk estimation relative to larger trends and intraday seasonalities at both market and sectoral levels.

Proof search in non-confluent tableau calculi, such as the connection tableau calculus, suffers from excess backtracking, but simple restrictions on backtracking are incomplete. We adopt constraint learning to reduce backtracking in the classical first-order connection calculus, while retaining completeness. An initial constraint learning language for connection-driven search is iteratively refined to greatly reduce backtracking in practice. The approach may be useful for proof search in other non-confluent tableau calculi.

Understanding lattice dynamics is central to elucidating the microscopic origin of charge density waves (CDWs), particularly in materials where electron-phonon coupling can play a dominant role. Raman spectroscopy, combined with first-principles calculations, offers a direct means to identify the vibrational modes involved and to monitor their evolution under controlled perturbations. In this work, we combine density functional theory calculations and Raman spectroscopy measurements to investigate the vibrational properties of the quasi-one-dimensional transition metal tetrachalcogenides TaTe$_4$ and NbTe$_4$, as well as their solid solutions Ta$_{1-x}$Nb$_{x}$Te$_4$ ($x$ = 0.0 - 1.0). For the stoichiometric compounds, first-principles calculations predict a phonon instability consistent with the trimerization associated with the CDW phase, providing theoretical evidence for the lattice distortion driving the transition. The calculated Raman-active modes show good agreement with room-temperature experimental spectra, enabling a systematic assignment of the observed peaks. Across the solid solution, most Raman modes evolve smoothly with composition. In contrast, the highest-frequency E$_{g}$ mode, dominated by transition-metal motion, exhibits a distinct behavior: its frequency remains close to that of the parent compounds while its intensity redistributes with stoichiometry. This evolution highlights the short-range character of this vibrational mode and suggests its relevance to the CDW-related lattice distortion in these materials.

Using strong-coupling time-dependent perturbation theory, we study the response of Mott and charge-transfer insulators to an oscillating electric field. We derive analytical expressions for the resulting higher-harmonic currents and show that they encode information about spin order and microscopic hopping pathways. The results demonstrate that higher harmonics can serve as probes of correlated materials and as sensors of the applied driving field.

There exists algorithms to detect reversibility of cellular automaton (CA) for both finite and infinite lattices taking quadratic time. But, can we identify a $d$-state CA rule in constant time that is always reversible for every lattice size $n\in \mathbb{N}$? To address this issue, this paper explores the reversibility properties of a subset of one-dimensional, $3$-neighborhood, $d$-state finite cellular automata (CAs), known as the first degree cellular automata (FDCAs) for any number of cells $(n\in \mathbb{N})$ under the null boundary condition. {In a first degree cellular automaton (FDCA), the local rule is defined using eight parameters. To ensure that the global transition function of $d$-state FDCA is reversible for any number of cells $(n\in \mathbb{N})$, it is necessary and sufficient to verify only three algebraic conditions among the parameter values. Based on these conditions, for any given $d$, one can synthesize all reversible FDCAs rules. Similarly, given a FDCA rule, one can check these conditions to decide its reversibility in constant time.

Pinching antenna systems (PAS) have recently emerged as a promising architecture for flexible and reconfigurable wireless communications. However, their performance is fundamentally constrained by in-waveguide attenuation, which is non-negligible in practical dielectric waveguides and can severely degrade the achievable data rate, particularly for long waveguides. To overcome this limitation, we propose a dual-fed PAS (DF-PAS), in which each waveguide is equipped with two feed points located at the two ends, enabling dynamic feed-point selection based on user locations. This design effectively shortens the in-waveguide propagation distance and mitigates attenuation-induced power loss without modifying the waveguide structure or the PA actuation mechanism. We investigate the DF-PAS in both single- and multi-waveguide scenarios. For the single-waveguide case, we derive closed-form high-SNR approximations of the ergodic rate and obtain closed-form solutions for the optimal PA position and feed-point selection under time-division multiple access (TDMA). We then extend DF-PAS to a multi-waveguide scenario, where we first derive closed-form high-SNR approximations of the ergodic rate and then formulate a joint optimization problem over feed-point selection, PA placement, and beamforming under general orthogonal multiple access (OMA). To solve this problem efficiently, we develop a two-phase optimization framework that integrates greedy feed-point switching, gradient-based PA placement, and WMMSE-based beamforming. Simulation results demonstrate that the proposed DF-PAS consistently outperforms conventional single-fed PAS (SF-PAS) across various network configurations, validating its effectiveness as a practical and scalable solution for mitigating in-waveguide attenuation in PAS-enabled wireless networks.

Empirical and LLM-based research in model-driven engineering increasingly relies on datasets of software models, for instance, to train or evaluate machine learning techniques for modeling support. These datasets have a significant impact on solution performance; hence, they should be treated and assessed as first-class artifacts. However, such datasets are typically collected or created ad hoc and without guarantees of their quality for the specific task for which they are used. This limits the comparability of results between studies, obscures dataset quality and representativeness, and leads to weak reproducibility and potential bias. In this work, we propose a benchmarking framework for model datasets (i.e., benchmarking the dataset itself). Benchmarking datasets involves systematically measuring their quality, representativeness, and suitability for specific tasks. To this end, we propose a Benchmark Platform for MDE that provides a unified infrastructure for systematically assessing and comparing datasets of software models across languages and formats, using defined criteria and metrics.

Recent advancements in quantum technologies have highlighted the importance of mitigating system imperfections, including parameter uncertainties and decoherence effects, to improve the performance of experimental platforms. However, most of the previous efforts in quantum control are devoted to the realization of arbitrary unitary operations in a closed quantum system. Here, we improve the algorithm that suppresses system imperfections and noises, providing notably enhanced scalability for robust and optimal control of open quantum systems. Through experimental validation in a superconducting quantum circuit, we demonstrate that our approach outperforms its conventional counterpart for closed quantum systems with an ultra-low infidelity of about $0.60\%$, while the complexity of this algorithm exhibits the same scaling, with only a modest increase in the prefactor. This work represents a notable advancement in quantum optimal control techniques, paving the way for realizing quantum-enhanced technologies in practical applications.

In quark-gluon plasma (QGP), at high temperatures $T$ the spontaneous generation of color magnetic fields, $b^3(T), b^8(T) \not = 0$ (3, 8 are color indexes), and usual magnetic field $b(T) \not = 0$ happens. Also, the Polyakov loop and related to it the $A_0(T)$ condensate, which is solution to Yang-Mills imaginary time equations, create. Recently, with the new type two-loop effective potential, which generalizes the known integral representation for the Bernoulli polynomials and takes into consideration the magnetic background, these effects were derived. The corresponding effective potential $W(T, b^3, b^8, b, A_0 )$ was calculated either in SU(2) gluodynamics or full quantum chromodynamics (QCD). The values of magnetic field strengths at different temperatures were calculated and the mechanism for stabilizing the background due to $A_0(T)$ was also discovered. In present paper, we concentrate on the one-loop quark contributions. In particular, we derive the effective vertexes, which couple magnetic fields and $A_0$. The vertexes result in new specific effects signalling the creation of QGP in heavy ion collision experiments. Key words: spontaneous magnetization, high temperature, asymptotic freedom, effective potential, $A_0$ condensate, effective vertexes.

We show that the ultralimit of a bounded sequence of Lipschitz maps into pointed metric spaces extends naturally to $p$-bounded sequences of Sobolev maps and that this ultralimit for Sobolev maps enjoys desirable properties. We use this to prove the stability of Dehn functions under ultraconvergence of pointed length spaces, thus resolving a problem posed by several researchers in the field. As an application, we obtain a simpler proof of a recent result of Stadler--Wenger, previously proved in the locally compact case by Lytchak--Wenger, characterizing spaces of curvature bounded above by $κ$ via an isoperimetric inequality for curves.

In this paper, we compute universal estimates of eigenvalues for a class of coupled systems of elliptic differential equations in divergence form on a bounded domain in Euclidean space, which includes the well-known Lamé and the Laplacian operator. Furthermore, we also give universal estimates of eigenvalues for a class of fourth-order elliptic differential problems in divergence form, which encloses the well-known bi-Laplacian operator. In both cases, as applications, we obtain the gap between consecutive eigenvalues as well as an upper bound for each eigenvalue.

We consider drift parameter estimation in a model driven by the sum of two independent fractional Brownian motions with different Hurst indices. Although the maximum likelihood estimator (MLE) for this model is known theoretically, its computation requires solving an operator equation involving fractional covariance operators. We develop an effective numerical method for approximating the solution of this equation by reformulating it as a Fredholm integral equation of the second kind with a weakly singular kernel. The resulting algorithm enables practical computation of the MLE. Numerical experiments illustrate the performance of the method.

We present a combined analytical and numerical study of the effective action of field theory cosmic strings in the Abelian-Higgs model in flat space. Starting directly from the underlying solitonic field theory description, we provide a systematic derivation of the low energy effective action and present evidence for the absence of nontrivial curvature correction terms when only the translational Goldstone modes are retained. Using this framework, we extend the effective theory to include higher energy fluctuations of the soliton profile, which map to massive degrees of freedom propagating on the worldsheet. We show that the leading curvature contribution enters only through the coupling between these massive modes and the worldsheet Ricci scalar. We validate the resulting effective theory via lattice simulations of the full field theory equations of motion in flat space, implemented with Adaptive Mesh Refinement to capture the string dynamics across different scales. The numerical simulations confirm the dynamics obtained using the effective action in its validity range. Furthermore, they also demonstrate the existence of the predicted parametric instability of excited strings that drives the transfer of energy from massive excitations to the Goldstone sector.

Focused ion beam irradiation of metastable Fe$_{78}$Ni$_{22}$ thin films grown on Cu(100) substrates results in the localized transformation of the originally paramagnetic, face-centered-cubic continuous film into ferromagnetic patterns with body-centered-cubic structure. The direction of the magnetic easy axis can be controlled by the focused ion beam scanning strategy, resulting in eight differently oriented crystallographic domains with different magnetic properties. We study the local crystallographic orientations of the transformed areas by electron backscatter diffraction and correlate these results with local magnetometry measurements. The observed magnetic anisotropy can be explained as a result of residual lattice strain after the fcc$\to$bcc transformation. These results extend the understanding of this material system and its transformation and allow for the patterning of high-quality magnetic nanostructures with precisely controlled magnetization landscapes.

Applications requiring real-time processing of large volumes of data have been the main driver for rethinking the traditional cloud, giving rise to novel cloud models. Distributed cloud (DC) is a model that allows users to dynamically create and dispose of strategically located ad-hoc clouds that contain resources best tailored to their needs. It is essential for this model to provide a high degree of observability for it to be viable in real-world scenarios. In this paper, we present the design and implementation of a monitoring system that collects metrics from DCs and makes them accessible to diverse clients. Agents running on nodes are responsible for collecting machine-, container-, and application-level metrics. During the health-check protocol, that data is transferred from the node to the DC's control plane running inside the cloud. There, it is persisted and served via multiple APIs, including a streaming API. Moreover, node metrics are aggregated for every DC in order to provide a more comprehensive view of the system's state.

Graphical methods for system analysis have played a central role in control theory. A recently emerging tool in this field is the Scaled Relative Graph (SRG). In this paper, we further extend its applicability by showing how the SRG of discrete-time linear-time-invariant (LTI) systems can be computed exactly from its state-space representation using linear matrix inequalities. We additionally propose a fully data-driven approach where we demonstrate how to compute the SRG exclusively from input-output data. Furthermore, we introduce a robust version of the SRG, which can be computed from noisy data trajectories and contains the SRG of the actual system.

We realize a homological block of a knot complement in $S^3$ for $G_{\mathbb{C}}=SL(2,\mathbb{C})$ as a half-index of a 3d $\mathcal{N}=2$ theory via an expression of the homological block as an inverted Habiro series by working out some examples, which we expect to extend to general knots. Also, by choosing a certain set of poles in the integral expression of the half-index, we obtain the colored Jones polynomial.

Urban mobility models are essential tools for understanding and forecasting how people and goods move within cities, which is vital for transportation planning. The spatial scale at which urban mobility is analysed is a crucial determinant of the insights gained from any model as it can affect models' performance. It is, therefore, important that urban mobility models should be assessed at appropriate spatial scales to reflect the underlying dynamics. In this study, we systematically evaluate the performance of three popular urban mobility models, namely gravity, radiation, and visitation models across spatial scales. The results show that while the visitation model consistently performs better than its gravity and radiation counterparts, their performance does not differ much when being assessed at some appropriate spatial scale common to all of them. Interestingly, at scales where all models perform badly, the visitation model suffers the most. Furthermore, results based on the conventional admin boundary may not perform so well as compared to distance-based clustering. The cross examination of urban mobility models across spatial scales also reveals the spatial organisation of the urban structure.

We use various analytical techniques to study Young Stellar Objects (YSOs) in an area of approximately $10' \times 10'$ in the IRAS 18456-0223 star-forming region. We use archival optical (Gaia DR3) and infrared (2MASS, UKIDSS, Spitzer, WISE, and Herschel) data, along with our optical spectroscopy of three bright stars for this purpose. We identify 89 YSOs (80 Class II and 9 Class I) based on their infrared properties. Our multiwavelength SED fits of selected YSOs show that they have masses $\sim 0.1$--$7.2$ $M_\odot$ and are up to $4$ Myr old. Our Minimum Spanning Tree (MST) analysis shows that these YSOs, situated at around 600 pc, form clusters with radial extents of order 0.5 pc and mean surface densities of $\sim 60$ pc$^{-2}$. We compare UKIDSS and 2MASS data of the YSOs and find that some of them show variability. We construct maps based on Herschel data which reveal multiple column density peaks ($N_{\rm H_2} \sim 10^{22}$ cm$^{-2}$) embedded in cold ($T_d \sim 10$--$13$ K) filaments. Our near-infrared extinction map exhibits several high-$A_V$ peaks, some of which coincide with the sub-mm column density maxima. Using our optical spectra of three bright sources, we show that they are of A--K spectral type. One star shows the Li I 6707 Å line, indicating its youth.

Plasma composition in the solar atmosphere differs between the photosphere and corona, producing an observable difference in elemental abundance known as the FIP effect. The FIP effect is characterised by the ratio of low to high FIP elements, giving a number known as the FIP bias. FIP bias values vary between different regions of the solar atmosphere, with typical observed values of $\sim$1 for coronal holes, $\sim$1.5-2 for the quiet Sun, and $\sim$3 for active regions. The Extreme ultraviolet Imaging Spectrometer (EIS) onboard the \emph{Hinode} spacecraft has enabled the widespread use of the Si X/S X line pair as a FIP bias diagnostic, but EIS observes other line pairs that can be used to estimate FIP bias. We consider three FIP bias diagnostics observed by \emph{Hinode}/EIS (Si X/S X, Ca XIV/Ar XIV, and Fe XVI/S XIII), comparing the FIP bias between Quiet Sun and an Active region. We also assume a range of signal-to-noise (SNR) cutoff values for each pixel, finding that while the SNR cutoff affects the number of useable pixels, higher (lower) SNR cutoffs remove (retain) a tail of high FIP bias values within the measured distribution. However, the median value of the FIP bias distribution remains largely unchanged. These results show the importance of a more nuanced view of FIP bias when using this vitally important diagnostic rather than a simplistic one-size-fits-all approach.

Cognitive warfare has emerged as a central feature of modern conflict, yet it remains inconsistently defined and difficult to evaluate. Existing approaches often treat cognitive operations as a subset of information operations, limiting the ability to assess cognitive attacker-defender interactions or determine when advantage has been achieved. This article proposes a unified definition of cognitive warfare, introduces an interaction framework grounded in the OODA loop, and identifies measurable attributes associated with cognitive superiority. To illustrate the use of the framework, a notional case study demonstrates how these concepts can be applied to assess cognitive attacks and defenses in a contested environment. Thus, the framework provides joint force leaders and analysts with a practical foundation for understanding, comparing, and evaluating cognitive warfare campaigns.

We consider a variant of VASS extended with integer counters, denoted VASS+Z. These are automata equipped with N and Z counters; the N-counters are required to remain nonnegative and the Z-counters do not have this restriction. We study the complexity of the reachability problem for VASS+Z when the number of N-counters is fixed. We show that reachability is NP-complete in 1-VASS+Z (i.e. when there is only one N-counter) regardless of unary or binary encoding. For $d \geq 2$, using a KLMST-based algorithm, we prove that reachability in d-VASS+Z lies in the complexity class $\mathcal{F}_{d+2}$. Our upper bound improves on the naively obtained Ackermannian complexity by simulating the Z-counters with N-counters. To complement our upper bounds, we show that extending VASS with integer counters significantly lowers the number of N-counters needed to exhibit hardness. We prove that reachability in unary 2-VASS+Z is PSPACE-hard; without Z-counters this lower bound is only known in dimension 5. We also prove that reachability in unary 3-VASS+Z is TOWER-hard. Without Z-counters, reachability in 3-VASS has elementary complexity and TOWER-hardness is only known in dimension 8.

The short lifespan of traditional data storage media, coupled with an exponential increase in storage demand, has made long-term archival a fundamental problem in the data storage industry and beyond. Consequently, researchers are looking for innovative media solutions that can store data over long time periods at a very low cost. DNA molecules, with their high density, long lifespan, and low energy needs, have emerged as a viable alternative to digital data archival. However, current DNA data storage technologies are facing challenges with respect to cost and reliability. Thus, coding rate and error robustness are critical to scale DNA storage and make it technologically and economically achievable. Moreover, the molecules of DNA that encode different files are often located in the same oligo pool. Without random access solutions at the oligo level, it is very impractical to decode a specific file from these mixed pools, as all oligos need to first be sequenced and decoded before a target file can be retrieved, which greatly deteriorates the read cost. This paper introduces a solution to efficiently encode and store images into DNA molecules, that aims at reducing the read cost necessary to retrieve a resolution-reduced version of an image. This image storage system is based on the Progressive Decoding Functionality of the JPEG2000 codec but can be adapted to any conventional progressive codec. Each resolution layer is encoded into a set of oligos using the JPEG DNA VM codec, a DNA-based coder that aims at retrieving a file with a high reliability. Depending on the desired resolution to be read, the set of oligos as well as the portion of the oligos to be sequenced and decoded are adjusted accordingly. These oligos will be selected at sequencing time, with the help of the adaptive sampling method provided by the Nanopore sequencers, making it a PCR-free random access solution.

Real-time city-scale traffic analytics requires processing 100s-1000s of CCTV streams under strict latency, bandwidth, and compute limits. We present a scalable AI-driven Intelligent Transportation System (AIITS) designed to address multi-dimensional scaling on an edge-cloud fabric. Our platform transforms live multi-camera video feeds into a dynamic traffic graph through a DNN inferencing pipeline, complemented by real-time nowcasting and short-horizon forecasting using Spatio-Temporal GNNs. Using a testbed to validate in a Bengaluru neighborhood, we ingest 100+ RTSP feeds from Raspberry Pis, while Jetson Orin edge accelerators perform high-throughput detection and tracking, producing lightweight flow summaries for cloud-based GNN inference. A capacity-aware scheduler orchestrates load-balancing across heterogeneous devices to sustain real-time performance as stream counts increase. To ensure continuous adaptation, we integrate SAM3 foundation-model assisted labeling and Continuous Federated Learning to update DNN detectors on the edge. Experiments show stable ingestion up to 2000 FPS on Jetson Orins, low-latency aggregation, and accurate and scalable ST-GNN forecasts for up to 1000 streams. A planned live demonstration will scale the full pipeline to 1000 streams, showcasing practical, cross-fabric scalability.

We report on the interaction between stimulated Brillouin scattering and temporal cavity solitons in doubly resonant ring resonators. Our experiments are performed in coherently driven passive optical-fibre resonators. We demonstrate that the interplay between four-wave mixing and cascade Brillouin lasing spontaneously generates patterns of CSs on a temporal grid at twice the Brillouin-shift. These patterns are shown to be highly stable owing to a long-range locking mechanism mediated by the acoustic oscillation generated by the solitons. We introduce a unified mean-field model of the cavity to describe the dynamics between the coupled forward and backward waves under coherent driving. This model reproduces very well the experiments and explains the paracrystalline structures of the soliton pattern. Our findings significantly advance the understanding of hybrid Brillouin-Kerr optical frequency combs.

The composition of plasma in the solar corona is characterised by the First Ionisation Potential (FIP) bias, and is thought to be the result of a ponderomotive force acting in the chromosphere to separate ionised from neutral plasma. Identifying potential signatures of this process in the solar chromosphere is the subject of active research. Full disk spectroheliograms of the chromosphere and transition region from the Interface Region Imaging Spectrometer (IRIS) spacecraft provide an opportunity to compare plasma signatures between active regions at different evolutionary stages and assess their relationship with the fractionation processes. Here we compare the C II, Si IV, and Mg II lines observed by IRIS, finding no clear variability between active regions at different evolutionary stages in the C II and Si IV lines. However, distinct differences can be identified between the active regions using the Mg II k/h ratio (which provides a proxy for plasma opacity). In particular, the regions with the highest median FIP bias exhibit double peaked distributions of plasma opacity, suggesting variable plasma density which could affect wave propagation in these locations. These results indicate that the relationship between the plasma properties and how the plasma is fractionated should be investigated in more detail by combining observations and modelling to better understand how it changes on both temporal and spatial scales