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A holographic CFT description of asymptotically flat spacetimes inherits vacuum degeneracies and IR divergences from its gravitational dual. We devise a Quantum Error Correcting (QEC) framework to encode both effects as correctable fluctuations on the CFT dual. The framework is physically motivated by embedding a chain of qudits in the so-called Klein spacetime and then taking a continuum $N\to \infty$ limit. At finite $N$ the qudit chain 1) enjoys a discrete version of celestial symmetries and 2) supports a Gottesman-Kitaev-Preskill (GKP) code. The limit results in hard states with quantized BMS hair in the celestial torus forming the logical subspace, robust under errors induced by soft radiation. Technically, the construction leverages the recently studied $w_{1+\infty}$ hierarchy of soft currents and its realization from a sigma model in twistor space.

A realization of gravitational amplitudes based in the large $N$ limit of a certain 2d $SU(N)$ Kac-Moody theory has been recently proposed. We relate this proposal to Color Kinematics (CK) duality and present an extension to EFT amplitudes for matter particles with any mass and spin. In particular, we recast these EFT amplitudes as celestial correlation functions and show they posses a chiral $w_{1+\infty}$ symmetry algebra if they are minimally coupled in the bulk. Massive states lead to an off-shell 1-parameter deformation of the algebra. Finally, we argue that in the limit $S\to\infty$ these states correspond to the Kerr black hole and we rediscover a classical $w_{1+\infty}$ action of Penrose., Comment: 20 pages

General Relativity famously predicts precession of orbital motions in the Schwarzschild metric. In this paper we show that by adding a NUT charge $N = iM$ the precession vanishes to all orders in $G$ even for rotating black holes. Moreover, we conjecture a generalization of the eikonal formula and show that the classical integrable trajectories determine the full quantum amplitude for this black hole, by means of exponentiation of the Post-Minkowskian radial action. Several consequences of integrability in self-dual gravity are discussed., Comment: 26 pages

In this talk, we present the CGC/saturation approach of Ref.[C.~Contreras, E.~Levin, R.~Meneses and M.~Sanhueza,Eur. Phys. J. C 80 (2020) no.11, 1029] and its parameters determined from the combined HERA data. This model features an analytical solution for the non-linear Balitsky-Kovchegov (BK) evolution equation and the exponential behavior of the saturation momentum on the impact parameter $b$-dependence, characterized by $Q_s\propto \exp(-mb)$. We compare our results with experimental data at small-$x$, including the proton structure function $F_2$, charm structure function $F_2^{c\bar{c}}$, and exclusive vector meson production. The model shows good agreement across a wide kinematic range. Our findings support using this approach for reliable predictions in upcoming experiments like the Electron-Ion Collider (EIC) and the LHeC., Comment: 8pp. 4 figures in pdf files

Wind downscaling is essential for improving the spatial resolution of weather forecasts, particularly in operational Numerical Weather Prediction (NWP). This study advances wind downscaling by extending the DownGAN framework introduced by Annau et al.,to operational datasets from the Global Deterministic Prediction System (GDPS) and High-Resolution Deterministic Prediction System (HRDPS), covering the entire Canadian domain. We enhance the model by incorporating high-resolution static covariates, such as HRDPS-derived topography, into a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty, implemented using a UNET-based generator. Following the DownGAN framework, our methodology integrates low-resolution GDPS forecasts (15 km, 10-day horizon) and high-resolution HRDPS forecasts (2.5 km, 48-hour horizon) with Frequency Separation techniques adapted from computer vision. Through robust training and inference over the Canadian region, we demonstrate the operational scalability of our approach, achieving significant improvements in wind downscaling accuracy. Statistical validation highlights reductions in root mean square error (RMSE) and log spectral distance (LSD) metrics compared to the original DownGAN. High-resolution conditioning covariates and Frequency Separation strategies prove instrumental in enhancing model performance. This work underscores the potential for extending high-resolution wind forecasts beyond the 48-hour horizon, bridging the gap to the 10-day low resolution global forecast window.

In autonomous navigation, trajectory replanning, refinement, and control command generation are essential for effective motion planning. This paper presents a resilient approach to trajectory replanning addressing scenarios where the initial planner's solution becomes infeasible. The proposed method incorporates a hybrid A* algorithm to generate feasible trajectories when the primary planner fails and applies a soft constraints-based smoothing technique to refine these trajectories, ensuring continuity, obstacle avoidance, and kinematic feasibility. Obstacle constraints are modelled using a dynamic Voronoi map to improve navigation through narrow passages. This approach enhances the consistency of trajectory planning, speeds up convergence, and meets real-time computational requirements. In environments with around 30\% or higher obstacle density, the ratio of free space before and after placing new obstacles, the Resilient Timed Elastic Band (RTEB) planner achieves approximately 20\% reduction in traverse distance, traverse time, and control effort compared to the Timed Elastic Band (TEB) planner and Nonlinear Model Predictive Control (NMPC) planner. These improvements demonstrate the RTEB planner's potential for application in field robotics, particularly in agricultural and industrial environments, where navigating unstructured terrain is crucial for ensuring efficiency and operational resilience.

Motion control is crucial for generating expressive and compelling video content; however, most existing video generation models rely mainly on text prompts for control, which struggle to capture the nuances of dynamic actions and temporal compositions. To this end, we train a video generation model conditioned on spatio-temporally sparse or dense motion trajectories. In contrast to prior motion conditioning work, this flexible representation can encode any number of trajectories, object-specific or global scene motion, and temporally sparse motion; due to its flexibility we refer to this conditioning as motion prompts. While users may directly specify sparse trajectories, we also show how to translate high-level user requests into detailed, semi-dense motion prompts, a process we term motion prompt expansion. We demonstrate the versatility of our approach through various applications, including camera and object motion control, "interacting" with an image, motion transfer, and image editing. Our results showcase emergent behaviors, such as realistic physics, suggesting the potential of motion prompts for probing video models and interacting with future generative world models. Finally, we evaluate quantitatively, conduct a human study, and demonstrate strong performance. Video results are available on our webpage: https://motion-prompting.github.io/, Comment: Project page: https://motion-prompting.github.io/

We present a suite of zoom-in cosmological simulations of Milky Way-like galaxies with a prominent disc component and a strong bar in their centre, based on a subsample of barred galaxies from the TNG50 magneto-hydrodynamic simulation. We modify the physical models that regulate star formation, namely, supernova feedback and black hole quasar feedback, to examine how they affect the disc and bar formation. We find that the morphology remains consistent in all the galaxies that develop a massive stellar disc ($>10^{10}\Msun$), which is dominant in comparison with the bulge mass. The black hole quasar feedback models used in this work do not affect bar formation, although they can affect the properties of the bar. The energy released by the supernovae causes a delay in the time of bar formation and, in models with strong feedback, stops bar formation. This could be understood since supernova feedback affects the assembly of the disc and bulge and their structural properties, such as the mass content, size, and radial velocity dispersion. We compared our predictions to three bar instability criteria proposed in the literature. We find that galaxies with varying supernovae and black hole quasar feedback satisfy these criteria at the moment of bar formation, except in extreme cases where the galaxy does not have or has weak supernova feedback, where for some galaxies, they fail to predict the existence (or no existence) of bars. Our findings provide insights into the physical process behind bar formation while putting constraints on the analytic prescriptions that predict bar formation., Comment: 21 pages, 11 figures, submitted to A&A. Comments are welcome

Detecting dual active galactic nuclei (DAGN) in observations and understanding theoretically which massive black holes (MBHs) compose them and in which galactic and large-scale environment they reside are becoming increasingly important questions as we enter the multi-messenger era of MBH astronomy. This paper presents the abundance and properties of DAGN produced in nine large-scale cosmological hydrodynamical simulations. We focus on DAGN powered by AGN with Lbol > 1e43 erg/s and belonging to distinct galaxies, i.e. pairs that can be characterised with current and near-future electromagnetic observations. We find that the number density of DAGN separated by a few to 30 proper kpc varies from 1e-8 (or none) to 1e-3 comoving Mpc^3 in the redshift range z=0-7. At a given redshift, the densities of the DAGN numbers vary by up to two orders of magnitude from one simulation to another. However, for all simulations, the DAGN peak is in the range z=1-3, right before the peak of cosmic star formation or cosmic AGN activity. The corresponding fractions of DAGN (with respect to the total number of AGN) range from 0 to 6 percent. We find that simulations could produce too few DAGN at z=0 (or merge pairs too quickly) compared to current observational constraints while being consistent with preliminary constraints at high redshift (z = 3). Next-generation observatories (e.g., AXIS) will be of paramount importance to detect DAGN across cosmic times. We predict the detectability of DAGN with future X-ray telescopes and discuss DAGN as progenitors for future LISA gravitational wave detections., Comment: Accepted for publication in MNRAS

In this study, we propose a novel method that integrates Nonlinear Model Predictive Contour Control (NMPCC) with an Exponentially Stabilizing Control Lyapunov Function (ES-CLF) and Exponential Higher-Order Control Barrier Functions to achieve stable path-following and obstacle avoidance in UAV systems. This framework enables unmanned aerial vehicles (UAVs) to safely navigate around both static and dynamic obstacles while strictly adhering to desired paths. The quaternion-based formulation ensures precise orientation and attitude control, while a robust optimization solver enforces the constraints imposed by the Control Lyapunov Function (CLF) and Control Barrier Functions (CBF), ensuring reliable real-time performance. The method was validated in a Model-in-the-Loop (MiL) environment, demonstrating effective path tracking and obstacle avoidance. The results highlight the framework's ability to minimize both orthogonal and tangential errors, ensuring stability and safety in complex environments., Comment: Submitted to IEEE Access for review

Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network. To minimize the cost of the AI inference task subject to requirements on latency, quality, and - crucially - reliability of the inference process, it is vital to optimize (i) the set of sensors/data sources and (ii) the DNN architecture, (iii) the network nodes executing sections of the DNN, and (iv) the resources to use. To this end, we leverage dynamic gated neural networks with branches, and propose a novel algorithmic strategy called Quantile-constrained Inference (QIC), based upon quantile-Constrained policy optimization. QIC makes joint, high-quality, swift decisions on all the above aspects of the system, with the aim to minimize inference energy cost. We remark that this is the first contribution connecting gated dynamic DNNs with infrastructure-level decision making. We evaluate QIC using a dynamic gated DNN with stems and branches for optimal sensor fusion and inference, trained on the RADIATE dataset offering Radar, LiDAR, and Camera data, and real-world wireless measurements. Our results confirm that QIC matches the optimum and outperforms its alternatives by over 80%.

This paper presents a comprehensive approach to nonlinear dynamics identification for UAVs using a combination of data-driven techniques and theoretical modeling. Two key methodologies are explored: Proportional-Derivative (PD) approximation and Sparse Identification of Nonlinear Dynamics (SINDy). The UAV dynamics are first modeled using the Euler-Lagrange formulation, providing a set of generalized coordinates. However, platform constraints limit the control inputs to attitude angles, and linear and angular velocities along the z-axis. To accommodate these limitations, thrust and torque inputs are approximated using a PD controller, serving as the foundation for nonlinear system identification. In parallel, SINDy, a data-driven method, is employed to derive a compact and interpretable model of the UAV dynamics from experimental data. Both identified models are then integrated into a Model Predictive Control (MPC) framework for accurate trajectory tracking, where model accuracy, informed by data-driven insights, plays a critical role in optimizing control performance. This fusion of data-driven approaches and theoretical modeling enhances the system's robustness and adaptability in real-world conditions, offering a detailed analysis of the UAV's dynamic behavior.

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.

We develop a method to design tunable quasiperiodic structures of particles suspended in a fluid by controlling standing acoustic waves. One application of our results is to ultrasound directed self-assembly, which allows fabricating composite materials with desired microstructures. Our approach is based on identifying the minima of a functional, termed the acoustic radiation potential, determining the locations of the particle clusters. This functional can be viewed as a two- or three-dimensional slice of a similar functional in higher dimensions as in the cut-and-project method of constructing quasiperiodic patterns. The higher dimensional representation allows for translations, rotations, and reflections of the patterns. Constrained optimization theory is used to characterize the quasiperiodic designs based on local minima of the acoustic radiation potential and to understand how changes to the controls affect particle patterns. We also show how to transition smoothly between different controls, producing smooth transformations of the quasiperiodic patterns. The developed approach unlocks a route to creating tunable quasiperiodic and moir\'e structures known for their unconventional superconductivity and other extraordinary properties. Several examples of constructing quasiperiodic structures, including in two and three dimensions, are given., Comment: 29 pages, 15 figures

Working with Resonance Chiral Theory, within the two resonance multiplets saturation scheme, we satisfy leading (and some subleading) chiral and asymptotic QCD constraints and accurately fit simultaneously the $\pi^{0},\eta,\eta^{\prime}$ transition form factors, for single and double virtuality. In the latter case, we supplement the few available measurements with lattice data to ensure a faithful description. Mainly due to the new results for the doubly virtual case, we improve over existing descriptions for the $\eta$ and $\eta^\prime$. Our evaluation of the corresponding pole contributions to the hadronic light-by-light piece of the muon $g-2$ read: $a_\mu^{\pi^{0}\text{-}\rm{pole}}=\left(61.9\pm0.6^{+2.4}_{-1.5}\right)\times10^{-11}$, $a_\mu^{\eta\text{-}\rm{pole}}=\left(15.2\pm0.5^{+1.1}_{-0.8}\right)\times10^{-11}$ and $a_\mu^{\eta^\prime\text{-}\rm{pole}}=\left(14.2\pm0.7^{+1.4}_{-0.9}\right)\times10^{-11}$, for a total of $a_\mu^{\pi^0+\eta+\eta^{\prime}\text{-}\rm{pole}}=\left(91.3\pm1.0^{+3.0}_{-1.9}\right)\times10^{-11}$, where the first and second errors are the statistical and systematic uncertainties, respectively., Comment: 49 pages, 8 figures. The correlations between the pseudo-data points from Lattice QCD were considered and the Global Analysis was changed accordingly in this version. Furthermore, the high energy behavior was analyzed in the whole spectrum of asymmetries between the single virtual and the double virtual case

Early-type dwarf galaxies constitute a prevalent population in the central regions of rich groups and clusters in the local Universe. These low-luminosity and low-mass stellar systems play a fundamental role in the assembly of the luminous galaxies observed today, according to the $\Lambda$CDM hierarchical theory. The origin of early-type dwarfs has been linked to the transformation of disk galaxies interacting with the intracluster medium, especially in dense environments. However, the existence of low-luminosity early-type galaxies in low-density environments presents a challenge to this scenario. This study presents a comprehensive photometric and spectroscopic analysis of the early-type dwarf galaxy CGCG014-074 using deep GEMINI+GMOS data, focusing on its peculiarities and evolutionary implications. CGCG014-074 exhibits distinct features, including a rotating inner disk, an extended stellar formation with a quiescent phase since about 2 Gyr ago, and the presence of boxy isophotes. From the kinematic analysis, we confirm CGCG014-074 as a nucleated early-type dwarf galaxy with embedded disk. The study of its stellar population parameters using different methods provides significant insights into the galaxy's evolutionary history. These results show an old and metal-poor nucleus ($\sim 9.3$ Gyr and $\mathrm{[Z/H]}\sim-0.84$ dex), while the stellar disk is younger ($\sim4.4$ Gyr) with a higher metallicity ($\mathrm{[Z/H]}\sim-0.40$ dex). These distinctive features collectively position CGCG014-074 as a likely building block galaxy that has evolved passively throughout its history., Comment: 11 pages, 8 figures. Accepted to be published in MNRAS

Throughout its history, the theory of quantum error correction has heavily benefited from translating classical concepts into the quantum setting. In particular, classical notions of weight enumerators, which relate to the performance of an error-correcting code, and MacWilliams' identity, which helps to compute enumerators, have been generalized to the quantum case. In this work, we establish a distinct relationship between the theoretical machinery of quantum weight enumerators and a seemingly unrelated physics experiment: we prove that Rains' quantum shadow enumerators - a powerful mathematical tool - arise as probabilities of observing fixed numbers of triplets in a Bell sampling experiment. This insight allows us to develop here a rigorous framework for the direct measurement of quantum weight enumerators, thus enabling experimental and theoretical studies of the entanglement structure of any quantum error-correcting code or state under investigation. On top of that, we derive concrete sample complexity bounds and physically-motivated robustness guarantees against unavoidable experimental imperfections. Finally, we experimentally demonstrate the possibility of directly measuring weight enumerators on a trapped-ion quantum computer. Our experimental findings are in good agreement with theoretical predictions and illuminate how entanglement theory and quantum error correction can cross-fertilize each other once Bell sampling experiments are combined with the theoretical machinery of quantum weight enumerators., Comment: 21+12 pages, 6+7 figures

The number of sensors deployed in the world is expected to explode in the near future. At this moment, nearly 30 billion Internet of Things (IoT) devices are connected and this number is expected to double in the next four years. While not all of these are battery powered, as technology becomes smaller and mobility becomes more important to consumers, soon a larger portion will be. This forecast predicts that the number of machine to machine (M2M) devices will have the largest increase, representing nearly 50% of all devices in 2023.

The Atacama Large Millimeter/submillimeter Array (ALMA) offers new diagnostic capabilities for studying the Sun, providing complementary insights through high spatial and temporal resolution at millimeter wavelengths. ALMA acts as a linear thermometer for atmospheric gas, aiding in understanding the solar atmosphere's structure, dynamics, and energy balance. Given the Sun's complex emission patterns and rapid evolution, high-cadence imaging is essential for solar observations. Snapshot imaging is required, though it limits available visibility data, making full exploitation of ALMA's capabilities non-trivial. Challenges in processing solar ALMA data highlight the need for revising and enhancing the solar observing mode. The ALMA development study High-Cadence Imaging of the Sun demonstrated the potential benefits of high cadence observations through a forward modelling approach. The resulting report provides initial recommendations for improved post-processing solar ALMA data and explores increasing the observing cadence to sub-second intervals to improve image reliability., Comment: ALMA Memo 628 summarising the results of the ESO-funded ALMA development study "High-cadence Imaging of the Sun", concluded in 2023 (74 pages, 53 figures)

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

We study the problem of finding the resistors in a resistor network from measurements of the power dissipated by the resistors under different loads. We give sufficient conditions for local uniqueness, i.e. conditions that guarantee that the linearization of this non-linear inverse problem admits a unique solution. Our method is inspired by a method to study local uniqueness of inverse problems with internal functionals in the continuum, where the inverse problem is reformulated as a redundant system of differential equations. We use our method to derive local uniqueness conditions for other discrete inverse problems with internal functionals including a discrete analogue of the inverse Schr\"odinger problem and problems where the resistors are replaced by impedances and dissipated power at the zero and a positive frequency are available. Moreover, we show that the dissipated power measurements can be obtained from measurements of thermal noise induced currents., Comment: 18 pages, 3 figures

We present a novel high-definition (HD) snapshot diffractive spectral imaging system utilizing a diffractive filter array (DFA) to capture a single image that encodes both spatial and spectral information. This single diffractogram can be computationally reconstructed into a spectral image cube, providing a high-resolution representation of the scene across 25 spectral channels in the 440-800 nm range at 1304x744 spatial pixels (~1 MP). This unique approach offers numerous advantages including snapshot capture, a form of optical compression, flexible offline reconstruction, the ability to select the spectral basis after capture, and high light throughput due to the absence of lossy filters. We demonstrate a 30-50 nm spectral resolution and compared our reconstructed spectra against ground truth obtained by conventional spectrometers. Proof-of-concept experiments in diverse applications including biological tissue classification, food quality assessment, and simulated stellar photometry validate our system's capability to perform robust and accurate inference. These results establish the DFA-based imaging system as a versatile and powerful tool for advancing scientific and industrial imaging applications., Comment: 33 pages, 16 figures

The oscillating magnetic field produced by unbalanced currents in radio-frequency ion traps induces transition frequency shifts and sideband transitions that can be harmful to precision spectroscopy experiments. Here, we describe a methodology, based on two-photon spectroscopy, for determining both the strength and direction of rf-induced magnetic fields without modifying any DC magnetic bias field or changing any trap RF power. The technique is readily applicable to any trapped-ion experiment featuring narrow linewidth transitions., Comment: 13 pages, 6 figures

From the implementation of the Grafcet programming language we obtain in this document the different ways of applying this method, the advantages it obtains with respect to efficiency, cost, quality and operational and time optimization. The behavior of a pump alternation process is analyzed using GRAFCET, which is easier to design, verify and implement sequential control systems. We will also have the different fields where this programming is applied., Comment: 8 PAGINAS, in Spanish language

Simulating large scenes with many rigid objects is crucial for a variety of applications, such as robotics, engineering, film and video games. Rigid interactions are notoriously hard to model: small changes to the initial state or the simulation parameters can lead to large changes in the final state. Recently, learned simulators based on graph networks (GNNs) were developed as an alternative to hand-designed simulators like MuJoCo and PyBullet. They are able to accurately capture dynamics of real objects directly from real-world observations. However, current state-of-the-art learned simulators operate on meshes and scale poorly to scenes with many objects or detailed shapes. Here we present SDF-Sim, the first learned rigid-body simulator designed for scale. We use learned signed-distance functions (SDFs) to represent the object shapes and to speed up distance computation. We design the simulator to leverage SDFs and avoid the fundamental bottleneck of the previous simulators associated with collision detection. For the first time in literature, we demonstrate that we can scale the GNN-based simulators to scenes with hundreds of objects and up to 1.1 million nodes, where mesh-based approaches run out of memory. Finally, we show that SDF-Sim can be applied to real world scenes by extracting SDFs from multi-view images.

The effective integration of generative artificial intelligence in education is a fundamental aspect to prepare future generations. The objective of this study is to analyze from a quantitative and qualitative point of view the perception of controlled student-IA interaction within the classroom. This analysis includes assessing the ethical implications and everyday use of AI tools, as well as understanding whether AI tools encourage students to pursue STEM careers. Several points for improvement in education are found, such as the challenge of getting teachers to engage with new technologies and adapt their methods in all subjects, not just those related to technologies., Comment: 17 pages, 7 figures, 4 tables

The fifth generation (5G) of cellular networks starts a paradigm shift from the traditional monolithic system design to a Service Based Architecture, that fits modern performance requirements and scales efficiently to new services. This paradigm will be the foundation of future cellular core networks beyond 5G. The new architecture splits network functionalities into smaller logical entities that can be disaggregated logically, physically, and geographically. This affords interoperability between the mobile network operators and commercial software and hardware vendors or cloud providers. By making use of commodity services and products, this system construct inherits the vulnerabilities in those underlying technologies, thereby increasing its attack surface and requiring a rigorous security analysis. In this work, we review the security implications introduced in B5G networks, and the security mechanisms that are supported by the 5G standard. We emphasize on the support of Zero Trust Architecture in 5G and its relevance in decentralized deployments. We revisit the definition of trust in modern enterprise network operations and identify important Zero Trust properties that are weakened by the nature of cloud deployments. To that end, we propose a vertical extension of Zero Trust, namely, Zero Trust Execution, to model untrusted execution environments, and we provide an analysis on how to establish trust in Beyond-5G network architectures using Trusted Execution Environments. Our analysis shows how our model architecture handles the increased attack surface and reinforces the Zero Trust Architecture principles in the 5G Core, without any changes to the 5G standard. Finally, we provide experimental results over a 5G testbed using Open5GS and UERANSIM that demonstrate minimal performance overhead, and a monetary cost evaluation., Comment: 18 pages, 8 figures

The diffusion of charged particles in a graph can be modeled using random walks on a weighted graph. We give strategies to hide (or cloak) changes in a subgraph from the perspective of measurements of expected net particle charges made at nodes away from the cloaked subgraph. We distinguish between passive and active strategies, depending on whether the strategy involves injecting particles. The passive strategy can hide topology and edge weight changes. In addition to these capabilities, the active strategy can also hide sources of particles, at the cost of prior knowledge of the expected net particle charges in the reference graph. The strategies we present rely on discrete analogues of classic potential theory, that include a Calder\'on calculus on graphs., Comment: 24 pages, 8 figures

Hemoglobin (Hb) is a multifaceted protein, classified as a metalloprotein, chromoprotein, and globulin. It incorporates iron, which plays a crucial role in transporting oxygen within red blood cells. Hb functions by carrying oxygen from the respiratory organs to diverse tissues in the body, where it releases oxygen to fuel aerobic respiration, thus supporting the organism's metabolic processes. Hb can exist in several forms, primarily distinguished by the oxidation state of the iron in the heme group, including Methemoglobin (MetHb). Measuring the concentration of MetHb is crucial because it cannot transport oxygen, hence higher concentration of MetHb in the blood causes methemoglobinemia. Here, we use optically detected magnetic relaxometry of paramagnetic iron spins in MetHb drop-casted onto nanostructured diamond doped with shallow high density nitrogen vacancy (NV) spin qubits. We modify the MetHb concentration in the range of 6 x 10^6 - 1.8 x 10^7 adsorbed Fe+3 spins per um^2 and observe an increase of the NV relaxation rate G1 (= 1/T1, T1 is NV spin lattice relaxation time) up to 2 x 10^3 s^-1. NV magnetic relaxometry of MetHb in phosphate-buffered saline solution shows a similar effect with an increase of G1 to 6.7 x 10e3 s^-1 upon increasing the MetHb concentration to 100 uM. The increase of NV G1 is explained by the increased spin noise coming from the Fe+3 spins present in MetHb proteins. This study presents an additional usage of NV quantum sensors to detect paramagnetic centers of biomolecules at volumes below 100 picoliter., Comment: arXiv admin note: text overlap with arXiv:2310.08605

Cell-Free Massive MIMO (CF mMIMO) has emerged as a potential enabler for future networks. It has been shown that these networks are much more energy-efficient than classical cellular systems when they are serving users at peak capacity. However, these CF mMIMO networks are designed for peak traffic loads, and when this is not the case, they are significantly over-dimensioned and not at all energy efficient. To this end, Adaptive Access Point (AP) ON/OFF Switching (ASO) strategies have been developed to save energy when the network is not at peak traffic loads by putting unnecessary APs to sleep. Unfortunately, the existing strategies rely on measuring channel state information between every user and every access point, resulting in significant measurement energy consumption overheads. Furthermore, the current state-of-art approach has a computational complexity that scales exponentially with the number of APs. In this work, we present a novel convex feasibility testing method that allows checking per-user Quality-of-Service (QoS) requirements without necessarily considering all possible access point activations. We then propose an iterative algorithm for activating access points until all users' requirements are fulfilled. We show that our method has comparable performance to the optimal solution whilst avoiding solving costly mixed-integer problems and measuring channel state information on only a limited subset of APs., Comment: 6 pages, 3 figures, accepted for presentation at EUCNC/6G Summit 2024

In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries. The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance. We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). Our method constructs a KG from historical issues for use in retrieval, retaining the intra-issue structure and inter-issue relations. During the question-answering phase, our method parses consumer queries and retrieves related sub-graphs from the KG to generate answers. This integration of a KG not only improves retrieval accuracy by preserving customer service structure information but also enhances answering quality by mitigating the effects of text segmentation. Empirical assessments on our benchmark datasets, utilizing key retrieval (MRR, Recall@K, NDCG@K) and text generation (BLEU, ROUGE, METEOR) metrics, reveal that our method outperforms the baseline by 77.6% in MRR and by 0.32 in BLEU. Our method has been deployed within LinkedIn's customer service team for approximately six months and has reduced the median per-issue resolution time by 28.6%.

Context. Recent studies of the optical depth comparing [12CII] and [13CII] line profiles in Galactic star-forming regions revealed strong self-absorption in [12CII] by low excitation foreground material, implying a large column density of C+ corresponding to an equivalent AV of a few, up to about 10 mag. Aims. As the nature and origin of such a large column of cold C+ foreground gas are difficult to explain, it is essential to constrain the physical conditions of this material. Methods. We conducted high-resolution observations of [OI] 63 um and [OI] 145 um lines in M17 SW and Mon R2. The [OI] 145 um transition traces warm PDR-material, while the [OI] 63 um line traces foreground material as manifested by absorption dips. Results. Comparison of both [OI] line profiles with [CII] isotopic lines confirms warm PDR-origin background emission and a significant column of cold foreground material causing self-absorption visible in [12CII] and [OI] 63 um profiles. In M17 SW, the C+ and O column densities are comparable for both layers. Mon R2 exhibits larger O columns compared to C+, indicating additional material where the carbon is neutral or in molecular form. Small-scale spatial variation of the foreground absorption profiles and the large column density (around 1E18 cm-2 ) of the foreground material suggest emission from high-density regions associated with the cloud complex, not a uniform diffuse foreground cloud. Conclusions. The analysis confirms that the previously detected intense [CII] foreground absorption is attributable to a large column of low excitation dense atomic material, where carbon is ionized, and oxygen is in neutral atomic form., Comment: 30 pages, 24 figures

We discuss a complementary asymptotic analysis of the so called minimal random walk. More precisely, we present a version of the almost sure central limit theorem as well as a generalization of the recently proposed quadratic strong laws. In addition, alternative demonstrations of the functional limit theorems will be supplied based on a P\'olya urn scheme instead of a martingale approach.

Motivations: Explainable Artificial Intelligence (XAI) systems aim to improve users' understanding of AI, but XAI research shows many cases of different explanations serving some users well and being unhelpful to others. In non-AI systems, some software practitioners have used inclusive design approaches and sometimes their improvements turned out to be "curb-cut" improvements -- not only addressing the needs of underserved users, but also making the products better for everyone. So, if AI practitioners used inclusive design approaches, they too might create curb-cut improvements, i.e., better explanations for everyone. Objectives: To find out, we investigated the curb-cut effects of inclusivity-driven fixes on users' mental models of AI when using an XAI prototype. The prototype and fixes came from an AI team who had adopted an inclusive design approach (GenderMag) to improve their XAI prototype. Methods: We ran a between-subject study with 69 participants with no AI background. 34 participants used the original version of the XAI prototype and 35 used the version with the inclusivity fixes. We compared the two groups' mental model concepts scores, prediction accuracy, and inclusivity. Results: We found four main results. First, it revealed several curb-cut effects of the inclusivity fixes: overall increased engagement with explanations and better mental model concepts scores, which revealed fixes with curb-cut properties. However (second), the inclusivity fixes did not improve participants' prediction accuracy scores -- instead, it appears to have harmed them. This "curb-fence" effect (opposite of the curb-cut effect) revealed the AI explanations' double-edged impact. Third, the AI team's inclusivity fixes brought significant improvements for users whose problem-solving styles had previously been underserved. Further (fourth), the AI team's fixes reduced the gender gap by 45%.

We present a complete characterization of the asymptotic behaviour of a correlated Bernoulli sequence { which depends on the parameter $\theta \in [0,1]$. A martingale theory based approach will allow} us to prove versions of the law of large numbers, quadratic strong law, law of iterated logarithm, almost sure central limit theorem and functional central limit theorem, in the case $\theta \le 1/2$. For $\theta > 1/2$, we will obtain a strong convergence to a non-degenerated random variable, including a central limit theorem and a law of iterated logarithm for the fluctuations.

This paper introduces a method for aligning solar observations from different telescopes. We utilized helioprojective coordinates from the Solar Dynamics Observatory (SDO) as a reference to align images from ALMA and IRIS. The alignment is based on correlation analysis, employing both the Pearson Correlation Coefficient (PCC) and the Structural Similarity Index (SSIM) for assessing data correlations. PCC was preferred for its effectiveness with diverse datasets and its computational efficiency compared to SSIM. The alignment demonstrated less than 1.0\,arcsec variation in average centers between the two methods, ensuring consistent results. We illustrate this method using ALMA observations, labeled D06 in the Solar ALMA Science Archive (SALSA), as a case study.

Development of a spatial-temporal and data-driven model of soil respiration at the global scale based on soil temperature, yearly soil moisture, and soil organic carbon (C) estimates. Prediction of soil respiration on an annual basis (1991-2018) with relatively high accuracy (NSE 0.69, CCC 0.82). Lower soil respiration trends, higher soil respiration magnitudes, and higher soil organic C stocks across areas experiencing the presence of sustainable soil management practices., Comment: The author was unaware that there was no legal rights to use a portion of the data used in this study