Gabriele Gianini

Quantum encrypted cloning encodes an unknown state into several encrypted components, each offering an alternative way to recover it later. We show that this redundancy can also serve for one-shot fault diagnosis: instead of inspecting the encrypted state, we measure relational Pauli observables testing consistency conditions imposed by the encoding. The canonical Yamaguchi--Kempf scheme encodes the input into n signal--key pairs, all carrying the same coherent Bell label, plus a transformed copy of the input qubit. Comparing the labels of two pairs yields a deterministic check that localizes an anomalous pair and identifies its Pauli-error class without revealing the label. Such checks cannot tell whether the signal or the key of that pair is faulty, and no measurement on the pairs can (we prove that they already generate the whole group of deterministic state-blind observables supported there, of rank 2(n-1)). This ambiguity matters: a faulty signal costs one redemption path, a faulty key threatens them all. However, retaining also the transformed input qubit contributes exactly two further independent checks, at any multiplicity, and these suffice to identify any single fault drawn from the single-qubit Pauli set. For three clones this gives six checks in all, and we prove that no smaller set of state-blind observables achieves the same resolution. Across clone multiplicities, parity governs how many components must be measured jointly to reach this resolution, not the resolution itself. These findings are then abstracted into a general framework - state-blind observables, deterministic healthy references, syndrome-induced fault partitions, redemption-oriented sufficiency - yielding relational diagnosability: a syndrome need not identify every fault, only enough of it to select a safe redemption path.

Accurate classification of clinically significant prostate cancer remains a major challenge. While multiparametric MRI (mpMRI) has improved lesion detection, effective categorization in accordance to the Prostate Imaging Reporting and Data System (PI-RADS) remains complex. In this study, we propose and evaluate three complementary approaches for automated PI-RADS classification differing in the way in which the features are extracted from the mpMRI imaging sequences. The first approach extracts hand-crafted radiomic features from manually segmented lesions using the PyRadiomics library. The second approach extends this by integrating fully automated lesion and zonal segmentation to simulate a real-world, manual-free pipeline. The third approach utilizes a custom convolutional neural network (CNN) to learn high-level features images and lesion masks directly. The images come from Apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI), and T2-weighted (T2W) imaging. The features issued by the three methods were used to train a set of machine learning models for multi-class PI-RADS classification, specifically targeting the clinically relevant categories 3, 4, and 5. Results show that ADC-derived features consistently yield superior performance, with one of the ensemble models reaching an AUC of 0.83. Combining features across all sequences further improved robustness (AUC = 0.84). PI-RADS 5 classification was most reliable (AUC ≥ 0.94), whereas PI-RADS 3 remained the most difficult to distinguish. Our findings highlight the effectiveness of ADC features and the advantage of combining automated and deep learning-based strategies for robust prostate cancer risk stratification.

Despite significant advances in teleoperated systems, optimal human performance remains a challenging task due to one key aspect, i.e. reduced situational awareness . Active telepresence is one of the major contributing factors affecting operator’s situational awareness. Providing the user with multiple perspectives of the remote environment can be very beneficial, although it may also introduce challenges in maintaining effective control. Prior work has largely examined discrete manipulation tasks using single-modality assessments, whereas this study applies a multimodal neuroergonomic approach to a continuous precision-cutting task, providing first insights into control–view alignment and multi-camera perception effects. In this work, we focus on two interaction and interface design factors: (a) control interaction factor , where we compare the effects of fixed and camera view-aligned control user interfaces on task-performance; (b) perception interface factor : the impact of different visual feedback configurations on operator attention and workload state, and interaction with the interface. A telerobotic system focused on a cutting task is chosen for this evaluation as it demands precision, depth perception, and also continuous feedback. A multimodal bio-sensor network is used to record operator-centric data, i.e Eye-tracking pupil measures, gaze measures and EEG signals, and assess the proposed interfaces using a neuroergonomics framework. User-study results show that using a view-aligned control frame in a multi-camera interface significantly improves cutting quality compared to using a fixed control frame, while the display of multiple camera perspectives in the same window is preferred to a single camera per window, promoting operator visual attention with no significant impact on the mental workload.

Extracting knowledge from business process logs to prevent violations of business rules can protect companies from major losses. Most of the existing approaches toward this goal focus on compliance verification with respect to a target business model and are purely reactive: they detect violations ex post . The few existing approaches that try to prevent violations beforehand require substantial manual intervention, don’t consider fine-grained logs, ordinarily found in real-world business scenarios, and are based on memoryless techniques. To fill these gaps, we propose an integrated end-to-end framework to predict business model violations from a stream of low-level event logs. We use a Bidirectional Long-Short-Term Memory (BiLSTM) model, integrated with an attention mechanism to capture discriminating features and enable training on long sequences. This framework, whose setup requires minimal human intervention, can forecast not only the type but also the relative location of the upcoming violations in the event sequence. This information is useful in determining the type of countermeasures that need to be taken. We demonstrate the applicability of the framework using a real-life event log and achieve a prediction accuracy of 99.74%.

Predicting flows that occur both through and around porous bodies is challenging due to coupled physics across fluid and porous regions and the need to generalize across diverse geometries and boundary conditions. We address this problem using two Physics Informed learning approaches: Physics Informed PointNets (PIPN) and Physics Informed Geometry Aware Neural Operator (P-IGANO). We enforce the incompressible Navier Stokes equations in the free-flow region and a Darcy Forchheimer extension in the porous region within a unified loss and condition the networks on geometry and material parameters. Datasets are generated with OpenFOAM on 2D ducts containing porous obstacles and on 3D windbreak scenarios with tree canopies and buildings. We first verify the pipeline via the method of manufactured solutions, then assess generalization to unseen shapes, and for PI-GANO, to variable boundary conditions and parameter settings. The results show consistently low velocity and pressure errors in both seen and unseen cases, with accurate reproduction of the wake structures. Performance degrades primarily near sharp interfaces and in regions with large gradients. Overall, the study provides a first systematic evaluation of PIPN/PI-GANO for simultaneous through-and-around porous flows and shows their potential to accelerate design studies without retraining per geometry.

Accurate delineation of lesions in prostate MRI is crucial for the diagnosis of prostate cancer. Manual segmentation is time-consuming, requires advanced medical expertise, and is subject to inter-operator variability. Automatic lesion segmentation therefore represents a valuable tool to support clinicians by reducing workload, minimizing observer bias, and enabling more consistent image analysis. In this work, we investigated the performance of four deep learning architectures for prostate lesion segmentation: nnU-Net, DenseUNet, SegResUNet, and U-Net. Unlike many existing studies that rely on publicly available data, we constructed a dedicated dataset to better capture real-world variability and challenges. The dataset, comprising T2-weighted (T2W), apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) sequences, was carefully annotated by medical experts to ensure high-quality labels. Training was performed using the full combination of these modalities. Two cohorts were considered based on lesion severity, as defined by PI-RADS (Prostate Imaging–Reporting and Data System) scores: one with only PI-RADS 4–5 lesions (151 patients), and another including PI-RADS 3 cases, totaling 209 patients. Evaluation was conducted both on a patient-by-patient basis and in a consolidated all-patient setting. In the patient-level analysis, nnU-Net achieved the highest Dice similarity coefficient (DSC) of 0.60 when trained on PI-RADS 4–5 lesions, while in the all-patient analysis, DenseUNet attained a DSC of 0.57 on the same dataset. These results are within the range reported in recent prostate lesion segmentation studies, and in some cases are comparable to or exceed those obtained with substantially larger datasets.