The Gaia Galactic survey mission is designed and optimized to obtain astrometry, photometry, and spectroscopy of nearly two billion stars in our Galaxy. Yet as an all-sky multi-epoch survey, Gaia also observes several million extragalactic objects down to a magnitude of G~21 mag. Due to the nature of the Gaia onboard selection algorithms, these are mostly point-source-like objects. Using data provided by the satellite, we have identified quasar and galaxy candidates via supervised machine learning methods, and estimate their redshifts using the low resolution BP/RP spectra. We further characterise the surface brightness profiles of host galaxies of quasars and of galaxies from pre-defined input lists. Here we give an overview of the processing of extragalactic objects, describe the data products in Gaia DR3, and analyse their properties. Two integrated tables contain the main results for a high completeness, but low purity (50-70%), set of 6.6 million candidate quasars and 4.8 million candidate galaxies. We provide queries that select purer sub-samples of these containing 1.9 million probable quasars and 2.9 million probable galaxies (both 95% purity). We also use high quality BP/RP spectra of 43 thousand high probability quasars over the redshift range 0.05-4.36 to construct a composite quasar spectrum spanning restframe wavelengths from 72-100 nm.

The present document delineates the analysis, design, implementation, and benchmarking of various neural network architectures within a short-term frequency prediction system for the foreign exchange market (FOREX). Our aim is to simulate the judgment of the human expert (technical analyst) using a system that responds promptly to changes in market conditions, thus enabling the optimization of short-term trading strategies. We designed and implemented a series of LSTM neural network architectures which are taken as input the exchange rate values and generate the short-term market trend forecasting signal and an ANN custom architecture based on technical analysis indicator simulators We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost.

Given several databases containing person-specific data held by different organizations, Privacy-Preserving Record Linkage (PPRL) aims to identify and link records that correspond to the same entity/individual across different databases based on the matching of personal identifying attributes, such as name and address, without revealing the actual values in these attributes due to privacy concerns. This reference work entry defines the PPRL problem, reviews the literature and key findings, and discusses applications and research challenges.

Pulsar Timing Array experiments probe the presence of possible scalar or pseudoscalar ultralight dark matter particles through decade-long timing of an ensemble of galactic millisecond radio pulsars. With the second data release of the European Pulsar Timing Array, we focus on the most robust scenario, in which dark matter interacts only gravitationally with ordinary baryonic matter. Our results show that ultralight particles with masses $10^{-24.0}~\text{eV} \lesssim m \lesssim 10^{-23.3}~\text{eV}$ cannot constitute $100\%$ of the measured local dark matter density, but can have at most local density $\rho\lesssim 0.3$ GeV/cm$^3$.

Observing and timing a group of millisecond pulsars (MSPs) with high rotational stability enables the direct detection of gravitational waves (GWs). The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of widely spaced pulsar-pairs. The Chinese Pulsar Timing Array (CPTA) is a collaboration aiming at the direct GW detection with observations carried out using Chinese radio telescopes. This short article serves as a `table of contents' for a forthcoming series of papers related to the CPTA Data Release 1 (CPTA DR1) which uses observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here, after summarizing the time span and accuracy of CPTA DR1, we report the key results of our statistical inference finding a correlated signal with amplitude $\log A_{\rm c}= -14.4 \,^{+1.0}_{-2.8}$ for spectral index in the range of $\alpha\in [-1.8, 1.5]$ assuming a GW background (GWB) induced quadrupolar correlation. The search for the Hellings-Downs (HD) correlation curve is also presented, where some evidence for the HD correlation has been found that a 4.6-$\sigma$ statistical significance is achieved using the discrete frequency method around the frequency of 14 nHz. We expect that the future International Pulsar Timing Array data analysis and the next CPTA data release will be more sensitive to the nHz GWB, which could verify the current results.

Studying atmospheric neutrino oscillations in the few-GeV range with a multimegaton detector promises to determine the neutrino mass hierarchy. This is the main science goal pursued by the future KM3NeT/ORCA water Cherenkov detector in the Mediterranean Sea. In this paper, the processes that limit the obtainable resolution in both energy and direction in charged-current neutrino events in the ORCA detector are investigated. These processes include the composition of the hadronic fragmentation products, the subsequent particle propagation and the photon-sampling fraction of the detector. GEANT simulations of neutrino interactions in seawater produced by GENIE are used to study the effects in the 1 - 20 GeV range. It is found that fluctuations in the hadronic cascade in conjunction with the variation of the inelasticity y are most detrimental to the resolutions. The effect of limited photon sampling in the detector is of significantly less importance. These results will therefore also be applicable to similar detectors/media, such as those in ice.

Document clustering is a traditional, efficient and yet quite effective, text mining technique when we need to get a better insight of the documents of a collection that could be grouped together. The K-Means algorithm and the Hierarchical Agglomerative Clustering (HAC) algorithm are two of the most known and commonly used clustering algorithms; the former due to its low time cost and the latter due to its accuracy. However, even the use of K-Means in text clustering over large-scale collections can lead to unacceptable time costs. In this paper we first address some of the most valuable approaches for document clustering over such 'big data' (large-scale) collections. We then present two very promising alternatives: (a) a variation of an existing K-Means-based fast clustering technique (known as BigKClustering - BKC) so that it can be applied in document clustering, and (b) a hybrid clustering approach based on a customized version of the Buckshot algorithm, which first applies a hierarchical clustering procedure on a sample of the input dataset and then it uses the results as the initial centers for a K-Means based assignment of the rest of the documents, with very few iterations. We also give highly efficient adaptations of the proposed techniques in the MapReduce model which are then experimentally tested using Apache Hadoop and Spark over a real cluster environment. As it comes out of the experiments, they both lead to acceptable clustering quality as well as to significant time improvements (compared to K-Means - especially the Buckshot-based algorithm), thus constituting very promising alternatives for big document collections.

Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Material and methods: We investigated the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, D Dimer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed. Results: The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. Discussion: The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model. Conclusion: Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.

Neutrino oscillations constitute an excellent tool to probe physics beyond the Standard Model. In this paper, we investigate the potential of the ESSnuSB experiment to constrain the effects of flavour-dependent long-range forces (LRFs) in neutrino oscillations, which may arise due to the extension of the Standard Model gauge group by introducing new $U(1)$ symmetries. Focusing on three specific $U(1)$ symmetries -- $L_e - L_\mu$, $L_e - L_\tau$, and $L_\mu - L_\tau$, we demonstrate that ESSnuSB offers a favourable environment to search for LRF effects. Our analyses reveal that ESSnuSB can set $90\%$ confidence level bounds of V_{e\mu} < 2.99 \times 10^{-14} \, \text{eV}, V_{e\tau} < 2.05 \times 10^{-14} \, \text{eV}, and V_{\mu\tau} < 1.81 \times 10^{-14} \, \text{eV}, which are competitive to the upcoming Deep Underground Neutrino Experiment (DUNE). It is also observed that reducing the systematic uncertainties from $5\%$ to $2\%$ improves the ESSnuSB limits on $V_{\alpha\beta}$. Interestingly, we find limited correlations between LRF parameters and the less constrained lepton mixing parameters $\theta_{23}$ and $\delta_{\text{CP}}$, preserving the robustness of ESSnuSB's sensitivity to CP violation. Even under extreme LRF potentials ($V_{\alpha\beta} \gg 10^{-13} \, \text{eV}$), the CP-violation sensitivity and $\delta_{\text{CP}}$ precision remain largely unaffected. These results establish ESSnuSB as a competitive experimental setup for probing LRF effects, complementing constraints from other neutrino sources and offering critical insights into the physics of long-range forces.

Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user's trust level. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency.

Neutrino oscillation experiments provide a unique window in exploring several new physics scenarios beyond the standard three flavour. One such scenario is quantum decoherence in neutrino oscillation which tends to destroy the interference pattern of neutrinos reaching the far detector from the source. In this work, we study the decoherence in neutrino oscillation in the context of the ESSnuSB experiment. We consider the energy-independent decoherence parameter and derive the analytical expressions for P$_{\mu e}$ and P$_{\mu \mu}$ probabilities in vacuum. We have computed the capability of ESSnuSB to put bounds on the decoherence parameters namely, $\Gamma_{21}$ and $\Gamma_{32}$ and found that the constraints on $\Gamma_{21}$ are competitive compared to the DUNE bounds and better than the most stringent LBL ones from MINOS/MINOS+. We have also investigated the impact of decoherence on the ESSnuSB measurement of the Dirac CP phase $\delta_{\rm CP}$ and concluded that it remains robust in the presence of new physics.

The effective management of Emergency Department (ED) overcrowding is essential for improving patient outcomes and optimizing healthcare resource allocation. This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital by leveraging the comprehensive MIMIC-IV dataset. After preprocessing the MIMIC-IV data, five algorithms were evaluated: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM Radial). Among these, RF demonstrated superior performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9999, sensitivity of 0.9997, and specificity of 0.9999 when applied to the MIMIC-IV data. These findings highlight the robustness of RF in handling complex datasets for admission prediction, establish MIMIC-IV as a valuable benchmark for validating models based on smaller local datasets, and provide actionable insights for improving ED management strategies.

In the effort to obtain a precise measurement of leptonic CP-violation with the ESS$\nu$SB experiment, accurate and fast reconstruction of detector events plays a pivotal role. In this work, we examine the possibility of replacing the currently proposed likelihood-based reconstruction method with an approach based on Graph Neural Networks (GNNs). As the likelihood-based reconstruction method is reasonably accurate but computationally expensive, one of the benefits of a Machine Learning (ML) based method is enabling fast event reconstruction in the detector development phase, allowing for easier investigation of the effects of changes to the detector design. Focusing on classification of flavour and interaction type in muon and electron events and muon- and electron neutrino interaction events, we demonstrate that the GNN reconstructs events with greater accuracy than the likelihood method for events with greater complexity, and with increased speed for all events. Additionally, we investigate the key factors impacting reconstruction performance, and demonstrate how separation of events by pion production using another GNN classifier can benefit flavour classification.

The detection of exceptionally high-energy {\gamma}-photons (up to 18 TeV) from GRB 221009A by the LHAASO Collaboration challenges conventional physics. Photon-axion-like particle (ALP) oscillations have been proposed to explain this anomaly, but they rely on specific parameter tuning. We present an alternative explanation involving superluminal dark photons. Building on the frameworks of Markoulakis and Valamontes, we propose that dark photons facilitated faster-than-light (FTL) propagation of information, allowing {\gamma}-photons to bypass extragalactic background light (EBL) attenuation. This hypothesis aligns with cosmological observations and experimental results, including those from the LHC, providing a robust framework for addressing the GRB 221009A anomaly.

Due to its critical role in cybersecurity, digital forensics has received significant attention from researchers and practitioners alike. The ever increasing sophistication of modern cyberattacks is directly related to the complexity of evidence acquisition, which often requires the use of several technologies. To date, researchers have presented many surveys and reviews on the field. However, such articles focused on the advances of each particular domain of digital forensics individually. Therefore, while each of these surveys facilitates researchers and practitioners to keep up with the latest advances in a particular domain of digital forensics, the global perspective is missing. Aiming to fill this gap, we performed a qualitative review of reviews in the field of digital forensics, determined the main topics on digital forensics topics and identified their main challenges. Our analysis provides enough evidence to prove that the digital forensics community could benefit from closer collaborations and cross-topic research, since it is apparent that researchers and practitioners are trying to find solutions to the same problems in parallel, sometimes without noticing it.

The discovery of the ubiquity of habitable extrasolar planets, combined with revolutionary advances in instrumentation and observational capabilities, have ushered in a renaissance in the millenia-old quest to answer our most profound question about the Universe and our place within it - Are we alone? The Breakthrough Listen Initiative, announced in July 2015 as a 10-year 100M USD program, is the most comprehensive effort in history to quantify the distribution of advanced, technologically capable life in the universe. In this white paper, we outline the status of the on-going observing campaign with our primary observing facilities, as well as planned activities with these instruments over the next few years. We also list collaborative facilities which will conduct searches for technosignatures in either primary observing mode, or commensally. We highlight some of the novel analysis techniques we are bringing to bear on multi-petabyte data sets, including machine learning tools we are deploying to search for a broader range of technosignatures than was previously possible.

We study charged particle production in proton-antiproton collisions at 300 GeV, 900 GeV, and 1.96 TeV. We use the direction of the charged particle with the largest transverse momentum in each event to define three regions of eta-phi space; toward, away, and transverse. The average number and the average scalar pT sum of charged particles in the transverse region are sensitive to the modeling of the underlying event. The transverse region is divided into a MAX and MIN transverse region, which helps separate the hard component (initial and final-state radiation) from the beam-beam remnant and multiple parton interaction components of the scattering. The center-of-mass energy dependence of the various components of the event are studied in detail. The data presented here can be used to constrain and improve QCD Monte Carlo models, resulting in more precise predictions at the LHC energies of 13 and 14 TeV.