Numerical security proofs based on conic optimization are known to deliver optimal secret-key rates, but so far they have mostly assumed that the emitted states are fully characterized. In practice, this assumption is unrealistic, since real devices inevitably suffer from imperfections and side channels that are extremely difficult to model in detail. Here, we extend conic-optimization methods to scenarios where only partial information about the emitted states is known, covering both prepare-and-measure and measurement-device-independent protocols. We demonstrate that our method outperforms state-of-the-art analytical and numerical approaches under realistic source imperfections, especially for protocols that use non-qubit encodings. These results advance numerical-based proofs towards a standard, implementation-ready framework for evaluating quantum key distribution protocols in the presence of source imperfections.

In this study, we report conclusive evidence for an ancient star cluster that has been accreted by the Large Magellanic Cloud (LMC). By leveraging observations from the Hubble Space Telescope (HST), we investigate the chrono-dynamical structure of a sample of seven old star clusters within the LMC in a self-consistent way. The multi-epoch nature of the dataset allowed the determination of high-precision proper motions for the clusters. Employing an isochrone-fitting methodology, we additionally infer from the deep high-resolution HST data homogeneous and robust estimates for their distances, ages and metallicities. Supplementing these data with literature line-of-sight velocities, we investigate the full 3-dimensional dynamics of the clusters within the frame of the LMC. With respect to the other clusters in our sample, NGC 1841 depicts a peculiar case. Its position in the age-metallicity plane, that makes it about 1 Gyr younger than the other metal-poor LMC clusters, but also its dynamical properties with a radial orbit almost perpendicular to the LMC disc plane, clearly advocates for a different origin. We thus conclude that NGC 1841 has likely been accreted by the LMC from a smaller galaxy. The other clusters in our sample show disc-like kinematics, with the case of NGC 2210 being peculiar, based on its inclined orbit. Their coherent age-metallicity relation closely resembles that of Gaia-Sausage-Enceladus globular clusters, thus suggesting a similar early evolution for the two dwarf galaxies. We do not find clear-cut chrono-kinematic evidence that NGC 2005 has been accreted by the LMC as suggested by a previous study based on its chemical abundance pattern. Regardless of its nature, its very old age illustrates that peculiar chemical evolutions already emerge at very early times.

User and Entity Behaviour Analytics (UEBA) is a broad branch of data analytics that attempts to build a normal behavioural profile in order to detect anomalous events. Among the techniques used to detect anomalies, Deep Autoencoders constitute one of the most promising deep learning models on UEBA tasks, allowing explainable detection of security incidents that could lead to the leak of personal data, hijacking of systems, or access to sensitive business information. In this study, we introduce the first implementation of an explainable UEBA-based anomaly detection framework that leverages Deep Autoencoders in combination with Doc2Vec to process both numerical and textual features. Additionally, based on the theoretical foundations of neural networks, we offer a novel proof demonstrating the equivalence of two widely used definitions for fully-connected neural networks. The experimental results demonstrate the proposed framework capability to detect real and synthetic anomalies effectively generated from real attack data, showing that the models provide not only correct identification of anomalies but also explainable results that enable the reconstruction of the possible origin of the anomaly. Our findings suggest that the proposed UEBA framework can be seamlessly integrated into enterprise environments, complementing existing security systems for explainable threat detection.

We introduce a simple and flexible concept for a heralded -- spectrally pure -- single photon source. The scheme uses a probabilistic photon pair source pumped with a CW laser, whereby a rapid gating InGaAs/InP single photon avalanche diode provides a synchronous clock and temporally resolves, and hence spectrally filters, the heralded photons. We demonstrate the concept by combining this with a narrow-band integrated silicon nitride photon-pair source. This simple architecture is capable of heralding photons with high spectral purity in the telecom band, but could be adapted to other wavelengths and bandwidth regimes.

We use a simple method to derive two concentration bounds on the hypergeometric distribution. Comparison with existing results illustrates the advantage of these bounds across different regimes.

We investigate diagonal artifacts present in images captured by several Samsung smartphones and their impact on PRNU-based camera source verification. We first show that certain Galaxy S series models share a common pattern causing fingerprint collisions, with a similar issue also found in some Galaxy A models. Next, we demonstrate that reliable PRNU verification remains feasible for devices supporting PRO mode with raw capture, since raw images bypass the processing pipeline that introduces artifacts. This option, however, is not available for the mid-range A series models or in forensic cases without access to raw images. Finally, we outline potential forensic applications of the diagonal artifacts, such as reducing misdetections in HDR images and localizing regions affected by synthetic bokeh in portrait-mode images.

We produce a clean and well-characterised catalogue of objects within 100\,pc of the Sun from the \G\ Early Data Release 3. We characterise the catalogue through comparisons to the full data release, external catalogues, and simulations. We carry out a first analysis of the science that is possible with this sample to demonstrate its potential and best practices for its use. The selection of objects within 100\,pc from the full catalogue used selected training sets, machine-learning procedures, astrometric quantities, and solution quality indicators to determine a probability that the astrometric solution is reliable. The training set construction exploited the astrometric data, quality flags, and external photometry. For all candidates we calculated distance posterior probability densities using Bayesian procedures and mock catalogues to define priors. Any object with reliable astrometry and a non-zero probability of being within 100\,pc is included in the catalogue. We have produced a catalogue of \NFINAL\ objects that we estimate contains at least 92\% of stars of stellar type M9 within 100\,pc of the Sun. We estimate that 9\% of the stars in this catalogue probably lie outside 100\,pc, but when the distance probability function is used, a correct treatment of this contamination is possible. We produced luminosity functions with a high signal-to-noise ratio for the main-sequence stars, giants, and white dwarfs. We examined in detail the Hyades cluster, the white dwarf population, and wide-binary systems and produced candidate lists for all three samples. We detected local manifestations of several streams, superclusters, and halo objects, in which we identified 12 members of \G\ Enceladus. We present the first direct parallaxes of five objects in multiple systems within 10\,pc of the Sun.

Innovation in Information and Communication Technology has become one of the key economic drivers of our technology dependent world. In popular notion, the tech industry or how ICT is often known has become synonymous to all technologies that drive modernity. Digital technologies have become so pervasive that it is hard to imagine new technology developments that are not totally or partially influenced by ICT innovations. Furthermore, the pace of innovation in ICT sector over the last few decades has been unprecedented in human history. In this paper we argue that, not only ICT had a tremendous impact on the way we communicate and produce but this innovation paradigm has crucially shaped collective expectations and imagination about what technology more broadly can actually deliver. These expectations have often crystalised into a widespread acceptance, among general public and policy makers, of technosolutionism. This is a belief that technology not restricted to ICT alone can solve all problems humanity is facing from poverty and inequality to ecosystem loss and climate change. In this paper we show the many impacts of relentless ICT innovation. The spectacular advances in this sector, coupled with corporate power that benefits from them have facilitated the uptake by governments and industries of an uncritical narrative of techno-optimist that neglects the complexity of the wicked problems that affect the present and future of humanity.

Cable-Driven Parallel Robots (CDPRs) are increasingly used for load manipulation tasks involving predefined toolpaths with intermediate stops. At each stop, where the platform maintains a fixed pose and the motors keep the cables under tension, the system must evaluate whether it is safe to proceed by detecting anomalies that could compromise performance (e.g., wind gusts or cable impacts). This paper investigates whether anomalies can be detected using only motor torque data, without additional sensors. It introduces an adaptive, unsupervised outlier detection algorithm based on Gaussian Mixture Models (GMMs) to identify anomalies from torque signals. The method starts with a brief calibration period, just a few seconds, during which a GMM is fit on known anomaly-free data. Real-time torque measurements are then evaluated using Mahalanobis distance from the GMM, with statistically derived thresholds triggering anomaly flags. Model parameters are periodically updated using the latest segments identified as anomaly-free to adapt to changing conditions. Validation includes 14 long-duration test sessions simulating varied wind intensities. The proposed method achieves a 100% true positive rate and 95.4% average true negative rate, with 1-second detection latency. Comparative evaluation against power threshold and non-adaptive GMM methods indicates higher robustness to drift and environmental variation.

With the recently increased interest in probabilistic models, the efficiency of an underlying sampler becomes a crucial consideration. A Hamiltonian Monte Carlo (HMC) sampler is one popular option for models of this kind. Performance of HMC, however, strongly relies on a choice of parameters associated with an integration method for Hamiltonian equations, which up to date remains mainly heuristic or introduce time complexity. We propose a novel computationally inexpensive and flexible approach (we call it Adaptive Tuning or ATune) that, by analyzing the data generated during a burning stage of an HMC simulation, detects a system specific splitting integrator with a set of reliable HMC hyperparameters, including their credible randomization intervals, to be readily used in a production simulation. The method automatically eliminates those values of simulation parameters which could cause undesired extreme scenarios, such as resonance artifacts, low accuracy or poor sampling. The new approach is implemented in the in-house software package \textsf{HaiCS}, with no computational overheads introduced in a production simulation, and can be easily incorporated in any package for Bayesian inference with HMC. The tests on popular statistical models using original HMC and generalized Hamiltonian Monte Carlo (GHMC) reveal the superiority of adaptively tuned methods in terms of stability, performance and accuracy over conventional HMC tuned heuristically and coupled with the well-established integrators. We also claim that the generalized formulation of HMC, i.e. GHMC, is preferable for achieving high sampling performance. The efficiency of the new methodology is assessed in comparison with state-of-the-art samplers, e.g. the No-U-Turn-Sampler (NUTS), in real-world applications, such as endocrine therapy resistance in cancer, modeling of cell-cell adhesion dynamics and influenza epidemic outbreak.

A passive quantum key distribution (QKD) transmitter generates the quantum states prescribed by a QKD protocol at random, combining a fixed quantum mechanism and a post-selection step. By avoiding the use of active optical modulators externally driven by random number generators, passive QKD transmitters offer immunity to modulator side channels and potentially enable higher frequencies of operation. Recently, the first linear optics setup suitable for passive decoy-state QKD has been proposed. In this work, we simplify the prototype and adopt sharply different approaches for BB84 polarization encoding and decoy-state generation. On top of it, we elaborate a tight custom-made security analysis surpassing an unnecessary assumption and a post-selection step that are central to the former proposal.

Configuring the hybrid precoders and combiners in a millimeter wave (mmWave) multiuser (MU) multiple-input multiple-output (MIMO) system is challenging in frequency selective channels. In this paper, we develop a system that uses compressive estimation on the uplink to configure precoders and combiners for the downlink (DL). In the first step, the base station (BS) simultaneously estimates the channels from all the mobile stations (MSs) on each subcarrier. To reduce the number of measurements required, compressed sensing techniques are developed that exploit common support on the different subcarriers. In the second step, exploiting reciprocity and the channel estimates, the base station designs hybrid precoders and combiners. Two algorithms are developed for this purpose, with different performance and complexity tradeoffs: 1) a factorization of the purely digital solution, and 2) an iterative hybrid design. Extensive numerical experiments evaluate the proposed solutions comparing to state-of-the-art strategies, and illustrating design tradeoffs in overhead, complexity, and performance.

The system of globular clusters (GCs) in the Milky Way (MW) comprises a mixture of both in situ and accreted clusters. Tracing the origin of GCs provides invaluable insights into the formation history of the MW. However, reconciling diverse strands of evidence is often challenging. A notable example is NGC 288, where despite significant efforts in the literature, the available chrono-chemodynamical data have yet to provide a definitive conclusion regarding its origin. On the one hand, all post-Gaia dynamical studies indicate an accreted origin for NGC 288 from the Gaia-Sausage-Enceladus (GSE) dwarf galaxy. On the other hand, NGC 288 has been found to be 2.5 Gyr older than other GSE GCs at the same metallicity, this suggesting a different (and possibly in situ) origin. In this work, we address the unresolved question on the origin of NGC 288 by analyzing its chrono-chemical properties in an unprecedentedly homogeneous framework. First, we compare the location of NGC 288 in the age-metallicity plane with that of other two in situ GCs at similar metallicity, namely NGC 6218 and NGC 6362. The age estimates obtained within the homogeneous framework of the CARMA collaboration show that the three clusters are coeval, reinforcing the contrast with the dynamical interpretation. Then, we compare the abundances with a sample of in situ and accreted clusters at similar metallicity, finding again consistency with the chemistry of in situ systems. To reconcile these results with its orbital properties, we propose a scenario where NGC 288 formed in the proto-disc of the MW, and then was dynamically heated by the interaction with the GSE merger. This is a fate that resembles that of proto-disc stars undergoing the so-called Splash event. Therefore, NGC 288 demonstrates the importance of a homogeneous chrono-chemodynamical information in the interpretation of the origin of MW GCs.

Nowadays, Neural Networks are considered one of the most effective methods for various tasks such as anomaly detection, computer-aided disease detection, or natural language processing. However, these networks suffer from the ``black-box'' problem which makes it difficult to understand how they make decisions. In order to solve this issue, an R package called neuralGAM is introduced. This package implements a Neural Network topology based on Generalized Additive Models, allowing to fit an independent Neural Network to estimate the contribution of each feature to the output variable, yielding a highly accurate and interpretable Deep Learning model. The neuralGAM package provides a flexible framework for training Generalized Additive Neural Networks, which does not impose any restrictions on the Neural Network architecture. We illustrate the use of the neuralGAM package in both synthetic and real data examples.

We discuss the use of symmetries for analysing the structural identifiability and observability of control systems. Special emphasis is put on the role of discrete symmetries, in contrast to the more commonly studied continuous or Lie symmetries. We argue that discrete symmetries are the origin of parameters which are structurally locally identifiable, but not globally. We exploit this fact to present a methodology for structural identifiability analysis that detects such parameters and characterizes the symmetries in which they are involved. We demonstrate the use of our methodology by applying it to four case studies.

We investigate diagonal artifacts present in images captured by several Samsung smartphones and their impact on PRNU-based camera source verification. We first show that certain Galaxy S series models share a common pattern causing fingerprint collisions, with a similar issue also found in some Galaxy A models. Next, we demonstrate that reliable PRNU verification remains feasible for devices supporting PRO mode with raw capture, since raw images bypass the processing pipeline that introduces artifacts. This option, however, is not available for the mid-range A series models or in forensic cases without access to raw images. Finally, we outline potential forensic applications of the diagonal artifacts, such as reducing misdetections in HDR images and localizing regions affected by synthetic bokeh in portrait-mode images.

The chemo-physical parametrisation of stellar spectra is essential for understanding the nature and evolution of stars and of Galactic stellar populations. Gaia DR3 contains the parametrisation of RVS data performed by the General Stellar Parametriser-spectroscopy, module. Here we describe the parametrisation of the first 34 months of RVS observations. GSP-spec estimates the chemo-physical parameters from combined RVS spectra of single stars. The main analysis workflow described here, MatisseGauguin, is based on projection and optimisation methods and provides the stellar atmospheric parameters; the individual chemical abundances of N, Mg, Si, S, Ca, Ti, Cr, FeI, FeII, Ni, Zr, Ce and Nd; the differential equivalent width of a cyanogen line; and the parameters of a DIB feature. Another workflow, based on an artificial neural network, provides a second set of atmospheric parameters that are useful for classification control. We implement a detailed quality flag chain considering different error sources. With about 5.6 million stars, the Gaia DR3 GSP-spec all-sky catalogue is the largest compilation of stellar chemo-physical parameters ever published and the first one from space data. Internal and external biases have been studied taking into account the implemented flags. In some cases, simple calibrations with low degree polynomials are suggested. The homogeneity and quality of the estimated parameters enables chemo-dynamical studies of Galactic stellar populations, interstellar extinction studies from individual spectra, and clear constraints on stellar evolution models. We highly recommend that users adopt the provided quality flags for scientific exploitation . The Gaia DR3 GSP-spec catalogue is a major step in the scientific exploration of Milky Way stellar populations, confirming the Gaia promise of a new Galactic vision (abridged).

Terahertz communications are envisioned as a key enabler for 6G networks. The abundant spectrum available in such ultra high frequencies has the potential to increase network capacity to huge data rates. However, they are extremely affected by blockages, to the point of disrupting ongoing communications. In this paper, we elaborate on the relevance of predicting visibility between users and access points (APs) to improve the performance of THz-based networks by minimizing blockages, that is, maximizing network availability, while at the same time keeping a low reconfiguration overhead. We propose a novel approach to address this problem, by combining a neural network (NN) for predicting future user-AP visibility probability, with a probability threshold for AP reselection to avoid unnecessary reconfigurations. Our experimental results demonstrate that current state-of-the-art handover mechanisms based on received signal strength are not adequate for THz communications, since they are ill-suited to handle hard blockages. Our proposed NN-based solution significantly outperforms them, demonstrating the interest of our strategy as a research line.

We present the age determination of 13 globular clusters dynamically associated with the Gaia-Sausage-Enceladus (GSE) merger event, as part of the CARMA project effort to trace the Milky Way assembly history. We used deep and homogeneous archival $Hubble$ $Space$ $Telescope$ data, and applied isochrone-fitting to derive homogeneous age estimates. We find that the majority of the selected clusters form a well-defined age-metallicity relation, with a few outliers. Among these, NGC 288 and NGC 6205 are more than 2 Gyr older than the other GSE globular clusters at similar metallicity, and are therefore interpreted as of likely in-situ origin. Moreover, NGC 7099 is somewhat younger than the average GSE trend, this suggesting a possible alternative dwarf galaxy progenitor, while NGC 5286 is mildly older, as if its progenitor was characterised by an higher star-formation efficiency. Another remarkable feature of the resulting age-metallicity relation is the presence of two epochs of globular cluster formation, with a duration of $\sim0.3$ Gyr each and separated by $\sim2$ Gyr. These findings are in excellent agreement with the age-metallicity relation of halo field stars found by González-Koda et al., clearly hinting at episodic star-formation in GSE. The age of the two formation epochs is similar to the mean age of the two groups of in-situ globular clusters previously studied by CARMA. These epochs might therefore be precisely pinpointing two important dynamical events that GSE had with the Milky Way during its evolutionary history. Finally, we discuss the correlation between the recent determination of Si and Eu with the clusters age and origin.

Configuring the antenna arrays is the main source of overhead in millimeter wave (mmWave) communication systems. In high mobility scenarios, the problem is exacerbated, as achieving the highest rates requires frequent link reconfiguration. One solution is to exploit spatial congruence between signals at different frequency bands and extract mmWave channel parameters from side information obtained in another band. In this paper we propose the concept of out-of-band information aided mmWave communication. We analyze different strategies to leverage information derived from sensors or from other communication systems operating at sub-6 GHz bands to help configure the mmWave communication link. The overhead reductions that can be obtained when exploiting out-of-band information are characterized in a preliminary study. Finally, the challenges associated with using out-of-band signals as a source of side information at mmWave are analyzed in detail.