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Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last 25 years, there is no consensus on a dominating discovery algorithm. Selecting the most suitable discovery algorithm remains a challenge due to competing quality measures and diverse user requirements. Manually selecting the most suitable process discovery algorithm from a range of options for a given event log is a time-consuming and error-prone task. This paper introduces ProReco, a Process discovery Recommender system designed to recommend the most appropriate algorithm based on user preferences and event log characteristics. ProReco incorporates state-of-the-art discovery algorithms, extends the feature pools from previous work, and utilizes eXplainable AI (XAI) techniques to provide explanations for its recommendations., Comment: 8 pages, 5 figures, 9 references

The business processes of organizations may deviate from normal control flow due to disruptive anomalies, including unknown, skipped, and wrongly-ordered activities. To identify these control-flow anomalies, process mining can check control-flow correctness against a reference process model through conformance checking, an explainable set of algorithms that allows linking any deviations with model elements. However, the effectiveness of conformance checking-based techniques is negatively affected by noisy event data and low-quality process models. To address these shortcomings and support the development of competitive and explainable conformance checking-based techniques for control-flow anomaly detection, we propose a novel process mining-based feature extraction approach with alignment-based conformance checking. This variant aligns the deviating control flow with a reference process model; the resulting alignment can be inspected to extract additional statistics such as the number of times a given activity caused mismatches. We integrate this approach into a flexible and explainable framework for developing techniques for control-flow anomaly detection. The framework combines process mining-based feature extraction and dimensionality reduction to handle high-dimensional feature sets, achieve detection effectiveness, and support explainability. The results show that the framework techniques implementing our approach outperform the baseline conformance checking-based techniques while maintaining the explainable nature of conformance checking. We also provide an explanation of why existing conformance checking-based techniques may be ineffective., Comment: 16 pages, 9 figures, 7 tables, 56 references

Distributed integrated sensing and communication (ISAC) devices can overcome the traditional resolution limitations imposed by the signal bandwidth, cooperating to produce high-resolution images of the environment. However, existing phase-coherent imaging approaches are not suited to imaging multiple moving targets, since the Doppler effect causes a phase rotation that degrades the image focus and biases the targets' locations. In this paper, we propose MovISAC, the first coherent imaging method for moving targets using distributed asynchronous ISAC devices. Our approach obtains a set of high-resolution images of the environment, in which each image represents only targets moving with a selected velocity. To achieve this, MovISAC performs over-the-air (OTA) synchronization to compensate for timing, frequency, and phase offsets among distributed ISAC devices. Then, to solve the prohibitive complexity of an exhaustive search over the targets' velocities, we introduce a new association algorithm to match the Doppler shifts observed by each ISAC device pair to the corresponding spatial peaks obtained by standard imaging methods. This gives MovISAC the unique capability of pre-compensating the Doppler shift for each target before forming the images, thus recasting it from an undesired impairment to image formation to an additional means for resolving the targets. We perform extensive numerical simulations showing that our approach achieves extremely high resolution, superior robustness to clutter thanks to the additional target separation in the Doppler domain, and obtains cm- and cm/s-level target localization and velocity estimation errors. MovISAC significantly outperforms existing imaging methods that do not compensate for the Doppler shift, demonstrating up to 18 times lower localization error and enabling velocity estimation., Comment: 13 pages, 13 figures, 1 table

The interference of two indistinguishable photons from a photon pair on a balanced beam splitter, where each photon enters in one of the input ports, yields a maximally entangled N00N-state shared between the outputs of the beam splitter through Hong-Ou-Mandel interference. At the same time, interference between the two modes of a two-mode squeezed state - the generalized output of a parametric photon-pair source - yields two independent, hence separable, single-mode squeezed states. Notably, both experiments use the exact same experimental setup. We resolve this seeming contradiction by measuring photon-number resolved quantum interference of the two modes of a two-mode squeezed state from an engineered parametric down-conversion source and show that the notion of inseparability is a result of post-selection on the presence of photons. Indeed, the complete joint photon statistics including the vacuum component are separable, as expected from textbook calculations.

Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private local data. The DNN is partitioned in SL, with most layers residing on the server and a few initial layers and inputs on the client side. This configuration allows resource-constrained clients to participate in training and inference. However, the distributed architecture exposes SL to backdoor attacks, where malicious clients can manipulate local datasets to alter the DNN's behavior. Existing defenses from other distributed frameworks like Federated Learning are not applicable, and there is a lack of effective backdoor defenses specifically designed for SL. We present SafeSplit, the first defense against client-side backdoor attacks in Split Learning (SL). SafeSplit enables the server to detect and filter out malicious client behavior by employing circular backward analysis after a client's training is completed, iteratively reverting to a trained checkpoint where the model under examination is found to be benign. It uses a two-fold analysis to identify client-induced changes and detect poisoned models. First, a static analysis in the frequency domain measures the differences in the layer's parameters at the server. Second, a dynamic analysis introduces a novel rotational distance metric that assesses the orientation shifts of the server's layer parameters during training. Our comprehensive evaluation across various data distributions, client counts, and attack scenarios demonstrates the high efficacy of this dual analysis in mitigating backdoor attacks while preserving model utility., Comment: To appear at NDSS 2025; 18 pages, 6 Tables, and 11 figures

DarkSide-20k is a novel liquid argon dark matter detector currently under construction at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) that will push the sensitivity for Weakly Interacting Massive Particle (WIMP) detection into the neutrino fog. The core of the apparatus is a dual-phase Time Projection Chamber (TPC), filled with \SI{50} {tonnes} of low radioactivity underground argon (UAr) acting as the WIMP target. NUV-HD-Cryo Silicon Photomultipliers (SiPM)s designed by Fondazione Bruno Kessler (FBK) (Povo, Trento, Italy) were selected as the photon sensors covering two $10.5~\text{m}^2$ Optical Planes, one at each end of the TPC, and a total of $5~\text{m}^2$ photosensitive surface for the liquid argon veto detectors. This paper describes the Quality Assurance and Quality Control (QA/QC) plan and procedures accompanying the production of FBK~NUV-HD-Cryo SiPM wafers manufactured by LFoundry s.r.l. (Avezzano, AQ, Italy). SiPM characteristics are measured at 77~K at the wafer level with a custom-designed probe station. As of May~2024, 603 of the 1400 production wafers (43\% of the total) for DarkSide-20k were tested, including wafers from all 57 production Lots. The wafer yield is $93.6\pm2.5$\%, which exceeds the 80\% specification defined in the original DarkSide-20k production plan.

In Integrated Sensing And Communication (ISAC) systems, estimating the micro-Doppler (mD) spectrogram of a target requires combining channel estimates retrieved from communication with ad-hoc sensing packets, which cope with the sparsity of the communication traffic. Hence, the mD quality depends on the transmission strategy of the sensing packets, which is still a challenging problem with no known solutions. In this letter, we design a deep Reinforcement Learning (RL) framework that fragments such a problem into a sequence of simpler decisions and takes advantage of the mD temporal evolution for maximizing the reconstruction performance. Our method is the first that learns sampling patterns to directly optimize the mD quality, enabling the adaptation of ISAC systems to variable communication traffic. We validate the proposed approach on a dataset of real channel measurements, reaching up to 40% higher mD reconstruction accuracy and several times lower computational complexity than state-of-the-art methods., Comment: 6 pages, 3 figures, 1 table

The two-qubit controlled-not (C-NOT) gate is an essential component in the construction of a gate-based quantum computer. In fact, its operation, combined with single qubit rotations allows to realise any quantum circuit. Several strategies have been adopted in order to build quantum gates, among them the photonic one offers the dual advantage of excellent isolation from the external environment and ease of manipulation at the single qubit level. Here we adopt a scalable time-multiplexed approach in order to build a fully reconfigurable architecture capable of implementing a post-selected interferometric scheme that implements the C-NOT operation with a fidelity of $(93.8\pm1.4)\%$. We use our experimental platform to generate the four Bell states., Comment: 9 pages, 4 figures

Estimating the Doppler frequency shift caused by moving targets is one of the key objectives of Integrated Sensing And Communication (ISAC) systems. In the case of Wi-Fi sensing, a reliable estimation of the Doppler enables applications such as target classification, human activity recognition, and gait analysis. However, in practical scenarios, Doppler estimation is hindered by the movement of transmitter and receiver devices, and by the phase offsets caused by their clock asynchrony. Existing approaches have separately addressed these two aspects, either assuming clock-synchronous moving devices or asynchronous static ones. Jointly tackling device motion and clock asynchrony is extremely challenging, as the Doppler shift from device movement differs for each propagation path and the phase offsets are time-varying. In this paper, we present AsyMov, a method to estimate the bistatic Doppler frequency of a target and the device velocity in ISAC setups featuring mobile and asynchronous devices. Our method leverages the channel impulse response at the receiver, by originally exploiting the invariance of phase offsets across propagation paths and the bistatic geometry. Moreover, AsyMov handles irregular channel sampling in the time domain and can be seamlessly integrated with device velocity measurements obtained from onboard sensors (if available), to enhance its reliability. AsyMov is thoroughly characterized from a theoretical perspective, via numerical simulation, and experimentally, implementing it on an IEEE 802.11ay testbed. Numerical and experimental results show superior performance against traditional methods based on the discrete Fourier transform and are on par with scenarios featuring static ISAC devices., Comment: 13 pages, 13 figures, 3 tables

We present a one-dimensional (1-D) model composed of aligned, electrostatically interacting charged disks, conceived in order to address in a computable model the validity of the Bogoliubov assumption on the decay of particle correlations in the Born-Bogoliubov-Green-Kirkwood-Yvon hierarchy. This assumption is a basic premise of plasma kinetic theory. The disk model exhibits spatially 1-D features at short distances, but retains 3-D features at large distances. Here the collective dynamics of this model plasma is investigated by solving the corresponding Vlasov equation. In addition, the implementation of the model for the numerical validation of the Bogoliubov assumption is formulated.

Various kinds of uncertainty can occur in event logs, e.g., due to flawed recording, data quality issues, or the use of probabilistic models for activity recognition. Stochastically known event logs make these uncertainties transparent by encoding multiple possible realizations for events. However, the number of realizations encoded by a stochastically known log grows exponentially with its size, making exhaustive exploration infeasible even for moderately sized event logs. Thus, considering only the top-K most probable realizations has been proposed in the literature. In this paper, we implement an efficient algorithm to calculate a top-K realization ranking of an event log under event independence within O(Kn), where n is the number of uncertain events in the log. This algorithm is used to investigate the benefit of top-K rankings over top-1 interpretations of stochastically known event logs. Specifically, we analyze the usefulness of top-K rankings against different properties of the input data. We show that the benefit of a top-K ranking depends on the length of the input event log and the distribution of the event probabilities. The results highlight the potential of top-K rankings to enhance uncertainty-aware process mining techniques.

DarkSide-20k (DS-20k) is a dark matter detection experiment under construction at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It utilises ~100 t of low radioactivity argon from an underground source (UAr) in its inner detector, with half serving as target in a dual-phase time projection chamber (TPC). The UAr cryogenics system must maintain stable thermodynamic conditions throughout the experiment's lifetime of over 10 years. Continuous removal of impurities and radon from the UAr is essential for maximising signal yield and mitigating background. We are developing an efficient and powerful cryogenics system with a gas purification loop with a target circulation rate of 1000 slpm. Central to its design is a condenser operated with liquid nitrogen which is paired with a gas heat exchanger cascade, delivering a combined cooling power of more than 8 kW. Here we present the design choices in view of the DS-20k requirements, in particular the condenser's working principle and the cooling control, and we show test results obtained with a dedicated benchmarking platform at CERN and LNGS. We find that the thermal efficiency of the recirculation loop, defined in terms of nitrogen consumption per argon flow rate, is 95 % and the pressure in the test cryostat can be maintained within $\pm$(0.1-0.2) mbar. We further detail a 5-day cool-down procedure of the test cryostat, maintaining a cooling rate typically within -2 K/h, as required for the DS-20k inner detector. Additionally, we assess the circuit's flow resistance, and the heat transfer capabilities of two heat exchanger geometries for argon phase change, used to provide gas for recirculation. We conclude by discussing how our findings influence the finalisation of the system design, including necessary modifications to meet requirements and ongoing testing activities., Comment: 44 pages, 25 figures, 1 table. Updated to match the published journal version

Turbulence in classical fluids is characterized by persistent structures that emerge from the chaotic landscape. We investigate the analogous process in fully kinetic plasma turbulence by using high-resolution, direct numerical simulations in two spatial dimensions. We observe the formation of long-living vortices with a profile typical of macroscopic, magnetically dominated force-free states. Inspired by the Harris pinch model for inhomogeneous equilibria, we describe these metastable solutions with a self-consistent kinetic model in a cylindrical coordinate system centered on a representative vortex, starting from an explicit form of the particle velocity distribution function. Such new equilibria can be simplified to a Gold-Hoyle solution of the modified force-free state. Turbulence is mediated by the long-living structures, accompanied by transients in which such vortices merge and form self-similarly new metastable equilibria. This process can be relevant to the comprehension of various astrophysical phenomena, going from the formation of plasmoids in the vicinity of massive compact objects to the emergence of coherent structures in the heliosphere., Comment: 12 pages, 5 figures (Main Text), 3 figures (Appendices)

Integrated Sensing And Communication (ISAC ) systems are expected to perform accurate radar sensing while having minimal impact on communication. Ideally, sensing should only reuse communication resources, especially for spectrum which is contended by many applications. However, this poses a great challenge in that communication systems often operate on narrow subbands with low sensing resolution. Combining contiguous subbands has shown significant resolution gain in active localization. However, multiband ISAC remains unexplored due to communication subbands being highly sparse (non-contiguous) and affected by phase offsets that prevent their aggregation (incoherent). To tackle these problems, we design HiSAC, the first multiband ISAC system that combines diverse subbands across a wide frequency range to achieve super-resolved passive ranging. To solve the non-contiguity and incoherence of subbands, HiSAC combines them progressively, exploiting an anchor propagation path between transmitter and receiver in an optimization problem to achieve phase coherence. HiSAC fully reuses pilot signals in communication systems, it applies to different frequencies and can combine diverse technologies, e.g., 5G-NR and WiGig. We implement HiSAC on an experimental platform in the millimeter-wave unlicensed band and test it on objects and humans. Our results show it enhances the sensing resolution by up to 20 times compared to single-band processing while occupying the same spectrum., Comment: 15 pages, 15 figures

The dual-phase liquid argon time projection chamber is presently one of the leading technologies to search for dark matter particles with masses below 10 GeV/c$^2$. This was demonstrated by the DarkSide-50 experiment with approximately 50 kg of low-radioactivity liquid argon as target material. The next generation experiment DarkSide-20k, currently under construction, will use 1,000 times more argon and is expected to start operation in 2027. Based on the DarkSide-50 experience, here we assess the DarkSide-20k sensitivity to models predicting light dark matter particles, including Weakly Interacting Massive Particles (WIMPs) and sub-GeV/c$^2$ particles interacting with electrons in argon atoms. With one year of data, a sensitivity improvement to dark matter interaction cross-sections by at least one order of magnitude with respect to DarkSide-50 is expected for all these models. A sensitivity to WIMP--nucleon interaction cross-sections below $1\times10^{-42}$ cm$^2$ is achievable for WIMP masses above 800 MeV/c$^2$. With 10 years exposure, the neutrino fog can be reached for WIMP masses around 5 GeV/c$^2$., Comment: 13 pages, 4 figures, supplementary material (4 figures)