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This article presents a review about the main CERN n\_TOF contributions to the field of neutron-capture experiments of interest for $s$-process nucleosynthesis studies over the last 25 years, with special focus on the measurement of radioactive isotopes. A few recent capture experiments on stable isotopes of astrophysical interest are also discussed. Results on $s$-process branching nuclei are appropriate to illustrate how advances in detection systems and upgrades in the facility have enabled increasingly challenging experiments and, as a consequence, have led to a better understanding and modeling of the $s$-process mechanism of nucleosynthesis. New endeavors combining radioactive-ion beams from ISOLDE for the production of radioisotopically pure samples for activation experiments at the new NEAR facility at n\_TOF are briefly discussed. On the basis of these new exciting results, also current limitations of state-of-the-art TOF and activation techniques will be depicted, thereby showing the pressing need for further upgrades and enhancements on both facilities and detection systems. A brief account of the potential technique based on inverse kinematics for direct neutron-capture measurements is also presented., Comment: submitted to Eur. Phys. Jour. Topical Collection

The stochastic three points (STP) algorithm is a derivative-free optimization technique designed for unconstrained optimization problems in $\mathbb{R}^d$. In this paper, we analyze this algorithm for three classes of functions: smooth functions that may lack convexity, smooth convex functions, and smooth functions that are strongly convex. Our work provides the first almost sure convergence results of the STP algorithm, alongside some convergence results in expectation. For the class of smooth functions, we establish that the best gradient iterate of the STP algorithm converges almost surely to zero at a rate arbitrarily close to $o(\frac{1}{\sqrt{T}})$, where $T$ is the number of iterations. Furthermore, within the same class of functions, we establish both almost sure convergence and convergence in expectation of the final gradient iterate towards zero. For the class of smooth convex functions, we establish that $f(\theta^T)$ converges to $\inf_{\theta \in \mathbb{R}^d} f(\theta)$ almost surely at a rate arbitrarily close to $o(\frac{1}{T})$, and in expectation at a rate of $O(\frac{d}{T})$ where $d$ is the dimension of the space. Finally, for the class of smooth functions that are strongly convex, we establish that when step sizes are obtained by approximating the directional derivatives of the function, $f(\theta^T)$ converges to $\inf_{\theta \in \mathbb{R}^d} f(\theta)$ in expectation at a rate of $O((1-\frac{\mu}{dL})^T)$, and almost surely at a rate arbitrarily close to $o((1-\frac{\mu}{dL})^T)$, where $\mu$ and $L$ are the strong convexity and smoothness parameters of the function.

Image captioning creates informative text from an input image by creating a relationship between the words and the actual content of an image. Recently, deep learning models that utilize transformers have been the most successful in automatically generating image captions. The capabilities of transformer networks have led to notable progress in several activities related to vision. In this paper, we thoroughly examine transformer models, emphasizing the critical role that attention mechanisms play. The proposed model uses a transformer encoder-decoder architecture to create textual captions and a deep learning convolutional neural network to extract features from the images. To create the captions, we present a novel ensemble learning framework that improves the richness of the generated captions by utilizing several deep neural network architectures based on a voting mechanism that chooses the caption with the highest bilingual evaluation understudy (BLEU) score. The proposed model was evaluated using publicly available datasets. Using the Flickr8K dataset, the proposed model achieved the highest BLEU-[1-3] scores with rates of 0.728, 0.495, and 0.323, respectively. The suggested model outperformed the latest methods in Flickr30k datasets, determined by BLEU-[1-4] scores with rates of 0.798, 0.561, 0.387, and 0.269, respectively. The model efficacy was also obtained by the Semantic propositional image caption evaluation (SPICE) metric with a scoring rate of 0.164 for the Flicker8k dataset and 0.387 for the Flicker30k. Finally, ensemble learning significantly advances the process of image captioning and, hence, can be leveraged in various applications across different domains., Comment: 35 pages, 10 figures, 4 tables

Brain imaging plays a crucial role in the diagnosis and treatment of various neurological disorders, providing valuable insights into the structure and function of the brain. Techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) enable non-invasive visualization of the brain, aiding in the understanding of brain anatomy, abnormalities, and functional connectivity. However, cost and radiation dose may limit the acquisition of specific image modalities, so medical image synthesis can be used to generate required medical images without actual addition. In the medical domain, where obtaining labeled medical images is labor-intensive and expensive, addressing data scarcity is a major challenge. Recent studies propose using transfer learning to overcome this issue. This involves adapting pre-trained CycleGAN models, initially trained on non-medical data, to generate realistic medical images. In this work, transfer learning was applied to the task of MR-CT image translation and vice versa using 18 pre-trained non-medical models, and the models were fine-tuned to have the best result. The models' performance was evaluated using four widely used image quality metrics: Peak-signal-to-noise-ratio, Structural Similarity Index, Universal Quality Index, and Visual Information Fidelity. Quantitative evaluation and qualitative perceptual analysis by radiologists demonstrate the potential of transfer learning in medical imaging and the effectiveness of the generic pre-trained model. The results provide compelling evidence of the model's exceptional performance, which can be attributed to the high quality and similarity of the training images to actual human brain images. These results underscore the significance of carefully selecting appropriate and representative training images to optimize performance in brain image analysis tasks., Comment: 19 pages, 9 figures, 6 tables

Histopathological image analysis is a reliable method for prostate cancer identification. In this paper, we present a comparative analysis of two approaches for segmenting glandular structures in prostate images to automate Gleason grading. The first approach utilizes a hand-crafted learning technique, combining Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) texture descriptors to highlight spatial dependencies and minimize information loss at the pixel level. For machine driven feature extraction, we employ a U-Net convolutional neural network to perform semantic segmentation of prostate gland stroma tissue. Support vector machine-based learning of hand-crafted features achieves impressive classification accuracies of 99.0% and 95.1% for GLCM and LBP, respectively, while the U-Net-based machine-driven features attain 94% accuracy. Furthermore, a comparative analysis demonstrates superior segmentation quality for histopathological grades 1, 2, 3, and 4 using the U-Net approach, as assessed by Jaccard and Dice metrics. This work underscores the utility of machine-driven features in clinical applications that rely on automated pixel-level segmentation in prostate tissue images., Comment: 13 pages, 14 figures, 2 tables

The design of fast reactors burning MOX fuels requires accurate capture and fission cross sections. For the particular case of neutron capture on 242Pu, the NEA recommends that an accuracy of 8-12% should be achieved in the fast energy region (2 keV-500 keV) compared to their estimation of 35% for the current uncertainty. Integral irradiation experiments suggest that the evaluated cross section of the JEFF-3.1 library overestimates the 242Pu(n,{\gamma}) cross section by 14% in the range between 1 keV and 1 MeV. In addition, the last measurement at LANSCE reported a systematic reduction of 20-30% in the 1-40 keV range relative to the evaluated libraries and previous data sets. In the present work this cross section has been determined up to 600 keV in order to solve the mentioned discrepancies. A 242Pu target of 95(4) mg enriched to 99.959% was irradiated at the n TOF-EAR1 facility at CERN. The capture cross section of 242Pu has been obtained between 1 and 600 keV with a systematic uncertainty (dominated by background subtraction) between 8 and 12%, reducing the current uncertainties of 35% and achieving the accuracy requested by the NEA in a large energy range. The shape of the cross section has been analyzed in terms of average resonance parameters using the FITACS code as implemented in SAMMY, yielding results compatible with our recent analysis of the resolved resonance region.The results are in good agreement with the data of Wisshak and K\"appeler and on average 10-14% below JEFF-3.2 from 1 to 250 keV, which helps to achieve consistency between integral experiments and cross section data. At higher energies our results show a reasonable agreement within uncertainties with both ENDF/B-VII.1 and JEFF-3.2. Our results indicate that the last experiment from DANCE underestimates the capture cross section of 242Pu by as much as 40% above a few keV., Comment: 20 pages, 19 figures, submitted to Phys. Rev. C

Challenging neutron-capture cross-section measurements of small cross sections and samples with a very limited number of atoms require high-flux time-of-flight facilities. In turn, such facilities need innovative detection setups that are fast, have low sensitivity to neutrons, can quickly recover from the so-called $\gamma$-flash, and offer the highest possible detection sensitivity. In this paper, we present several steps toward such advanced systems. Specifically, we describe the performance of a high-sensitivity experimental setup at CERN n\_TOF EAR2. It consists of nine sTED detector modules in a compact cylindrical configuration, two conventional used large-volume C$_{6}$D$_{6}$ detectors, and one LaCl$_{3}$(Ce) detector. The performance of these detection systems is compared using $^{93}$Nb($n$,$\gamma$) data. We also developed a detailed \textsc{Geant4} Monte Carlo model of the experimental EAR2 setup, which allows for a better understanding of the detector features, including their efficiency determination. This Monte Carlo model has been used for further optimization, thus leading to a new conceptual design of a $\gamma$ detector array, STAR, based on a deuterated-stilbene crystal array. Finally, the suitability of deuterated-stilbene crystals for the future STAR array is investigaged experimentally utilizing a small stilbene-d12 prototype. The results suggest a similar or superior performance of STAR with respect to other setups based on liquid-scintillators, and allow for additional features such as neutron-gamma discrimination and a higher level of customization capability.

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output and, hence, are crucial for supporting decision-making. Despite their importance, the evaluation of these explanations often lacks grounding in user studies and remains fragmented, with existing metrics not fully capturing human perspectives. To address this challenge, we developed a diverse set of 30 counterfactual scenarios and collected ratings across 8 evaluation metrics from 206 respondents. Subsequently, we fine-tuned different Large Language Models (LLMs) to predict average or individual human judgment across these metrics. Our methodology allowed LLMs to achieve an accuracy of up to 63% in zero-shot evaluations and 85% (over a 3-classes prediction) with fine-tuning across all metrics. The fine-tuned models predicting human ratings offer better comparability and scalability in evaluating different counterfactual explanation frameworks., Comment: This paper extends the AAAI-2025 version by including the Appendix

The fidelity gap between simulation-trained vision-based data-driven cloth neural controllers and real-world operation impedes reliable deployment of methods from simulation into physical trials. Real-world grasping errors, such as misgrasping and multilayer grasping, degrade their performance; additionally, some fabrics made of synthetic material also tend to stick to the commonly employed Franka Emika Panda's original gripper. Different approaches adopted various strategies to resolve these problems, further complicating real-world comparison between state-of-the-art methods. We propose DeepCloth-ROB$^2_{\text{QS}}$P&P with a simulation-to-reality transfer strategy Towel-Sim2Real and a cloth grasping protocol to consider and mitigate these grasping errors for robustly deploying quasi-static pick-and-place neural controllers in cloth shaping and demonstrate its generalisability across different deep-learning methods, fabric contexts and robot platforms. Our approach allows us to compare multiple neural controllers in a real environment for the first time, offering valuable insights to the cloth manipulation community., Comment: 8 pages main texts, 3 figures, and 3 tables. It is submitted to the 2025 IEEE International Conference on Robotics & Automation (ICRA)

Ion and metal diffusion in metal halide perovskites, charge-transporting layers, and electrodes are detrimental to the performance and stability of perovskite-based photovoltaic devices. As a result, there is an intense research interest in developing novel defect and ion diffusion mitigation strategies. We present a simple, low-cost, scalable, and highly effective method that uses spray-coated exfoliated graphite interlayers to block ion and metal diffusion and humidity ingress within the perovskite, the hole transport material, and metal electrodes. The influence of inserting the exfoliated graphite films on the structural, surface morphology, and optoelectronic properties were examined through various methods, including X-ray diffraction, Time-of-Flight Secondary Ion Mass Spectrometry, Scanning electron microscope, atomic force microscopy, Current-voltage (J-V) characteristics, Transient photocurrent, and transient photovoltage. Our comprehensive investigation found that exfoliated graphite films reduced the I- and Li+ diffusion among the layers, leading to defect mitigation, reducing non-radiative recombination, and enhancing the device stability. Consequently, the best-performing device demonstrated a power conversion efficiency of 25% and a fill factor exceeding 80%. Additionally, these devices were subjected to different lifetime tests, which significantly enhanced the operational stability., Comment: 30 pages, 13 figures

The $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) cross-sections have been measured at the Experimental Area 2 (EAR2) of the n_TOF facility at CERN with three C$_6$D$_6$ detectors. This measurement is part of a collective effort to improve the capture cross-section data for Minor Actinides (MAs), which are required to estimate the production and transmutation rates of these isotopes in light water reactors and innovative reactor systems. In particular, the neutron capture in $^{246}$Cm and $^{248}$Cm open the path for the formation of other Cm isotopes and heavier elements such as Bk and Cf and the knowledge of (n,$\gamma$) cross-sections of these Cm isotopes plays an important role in the transport, transmutation and storage of the spent nuclear fuel. The reactions $^{246}$Cm(n,$\gamma$) and $^{248}$Cm(n,$\gamma$) have been the two first capture measurements analyzed at n_TOF EAR2. Until this experiment and two recent measurements performed at J-PARC, there was only one set of data of the capture cross-sections of $^{246}$Cm and $^{248}$Cm, that was obtained in 1969 in an underground nuclear explosion experiment. In the measurement at n_TOF a total of 13 resonances of $^{246}$Cm between 4 and 400 eV and 5 of $^{248}$Cm between 7 and 100 eV have been identified and fitted. The radiative kernels obtained for $^{246}$Cm are compatible with JENDL-5, but some of them are not with JENDL-4, which has been adopted by JEFF-3.3 and ENDF/B-VIII.0. The radiative kernels obtained for the first three $^{248}$Cm resonances are compatible with JENDL-5, however, the other two are not compatible with any other evaluation and are 20% and 60% larger than JENDL-5.

In this work, we focused on deep learning image processing in the context of oral rare diseases, which pose challenges due to limited data availability. A crucial step involves teeth detection, segmentation and numbering in panoramic radiographs. To this end, we used a dataset consisting of 156 panoramic radiographs from individuals with rare oral diseases and labeled by experts. We trained the Detection Transformer (DETR) neural network for teeth detection, segmentation, and numbering the 52 teeth classes. In addition, we used data augmentation techniques, including geometric transformations. Finally, we generated new panoramic images using inpainting techniques with stable diffusion, by removing teeth from a panoramic radiograph and integrating teeth into it. The results showed a mAP exceeding 0,69 for DETR without data augmentation. The mAP was improved to 0,82 when data augmentation techniques are used. Furthermore, we observed promising performances when using new panoramic radiographs generated with inpainting technique, with mAP of 0,76.

Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that meets students' needs. While learning management systems support teachers' productivity and creativity, they typically provide the same content to all learners in a course, ignoring their unique learning styles. To address this issue, we propose a semi-supervised machine learning approach that detects students' learning styles using a data mining technique. We use the commonly used Felder Silverman learning style model and demonstrate that our semi-supervised method can produce reliable classification models with few labeled data. We evaluate our approach on two different courses and achieve an accuracy of 88.83% and 77.35%, respectively. Our work shows that educational data mining and semi-supervised machine learning techniques can identify different learning styles and create a personalized learning environment., Comment: 14 pages, 12 figures, journal paper in IEEE Transactions on Learning Technologies

Generative Adversarial Networks (GANs) have exhibited noteworthy advancements across various applications, including medical imaging. While numerous state-of-the-art Deep Convolutional Neural Network (DCNN) architectures are renowned for their proficient feature extraction, this paper investigates their efficacy in the context of medical image deepfake detection. The primary objective is to effectively distinguish real from tampered or manipulated medical images by employing a comprehensive evaluation of 13 state-of-the-art DCNNs. Performance is assessed across diverse evaluation metrics, encompassing considerations of time efficiency and computational resource requirements. Our findings reveal that ResNet50V2 excels in precision and specificity, whereas DenseNet169 is distinguished by its accuracy, recall, and F1-score. We investigate the specific scenarios in which one model would be more favorable than another. Additionally, MobileNetV3Large offers competitive performance, emerging as the swiftest among the considered DCNN models while maintaining a relatively small parameter count. We also assess the latent space separability quality across the examined DCNNs, showing superiority in both the DenseNet and EfficientNet model families and entailing a higher understanding of medical image deepfakes. The experimental analysis in this research contributes valuable insights to the field of deepfake image detection in the medical imaging domain., Comment: 11 pages, 7 figures, 4 tables

Establishing quantum advantage for variational quantum algorithms is an important direction in quantum computing. In this work, we apply the Quantum Approximate Optimisation Algorithm (QAOA) -- a popular variational quantum algorithm for general combinatorial optimisation problems -- to a variant of the satisfiability problem (SAT): Not-All-Equal SAT (NAE-SAT). We focus on regimes where the problems are known to have solutions with low probability and introduce a novel classical solver that outperforms existing solvers. Extensively benchmarking QAOA against this, we show that while the runtime of both solvers scales exponentially with the problem size, the scaling exponent for QAOA is smaller for large enough circuit depths. This implies a polynomial quantum speedup for solving NAE-SAT., Comment: 10 pages

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather, to planning renewable energy use. Here, we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, the European Centre for Medium-Range Forecasts (ECMWF)'s ensemble forecast, ENS. Unlike traditional approaches, which are based on numerical weather prediction (NWP), GenCast is a machine learning weather prediction (MLWP) method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-hour steps and 0.25 degree latitude-longitude resolution, for over 80 surface and atmospheric variables, in 8 minutes. It has greater skill than ENS on 97.4% of 1320 targets we evaluated, and better predicts extreme weather, tropical cyclones, and wind power production. This work helps open the next chapter in operational weather forecasting, where critical weather-dependent decisions are made with greater accuracy and efficiency., Comment: Main text 11 pages, Appendices 76 pages

One of the critical aspects for the accurate determination of neutron capture cross sections when combining time-of-flight and total energy detector techniques is the characterization and control of systematic uncertainties associated to the measuring devices. In this work we explore the most conspicuous effects associated to harsh count rate conditions: dead-time and pile-up effects. Both effects, when not properly treated, can lead to large systematic uncertainties and bias in the determination of neutron cross sections. In the majority of neutron capture measurements carried out at the CERN n\_TOF facility, the detectors of choice are the C$_{6}$D$_{6}$ liquid-based either in form of large-volume cells or recently commissioned sTED detector array, consisting of much smaller-volume modules. To account for the aforementioned effects, we introduce a Monte Carlo model for these detectors mimicking harsh count rate conditions similar to those happening at the CERN n\_TOF 20~m fligth path vertical measuring station. The model parameters are extracted by comparison with the experimental data taken at the same facility during 2022 experimental campaign. We propose a novel methodology to consider both, dead-time and pile-up effects simultaneously for these fast detectors and check the applicability to experimental data from $^{197}$Au($n$,$\gamma$), including the saturated 4.9~eV resonance which is an important component of normalization for neutron cross section measurements.

The Experimental Cavern North 3 (ECN3) is an underground experimental cavern on the CERN Pr\'evessin site. ECN3 currently hosts the NA62 experiment, with a physics programme devoted to rare kaon decays and searches of hidden particles approved until Long Shutdown 3 (LS3). Several options are proposed on the longer term in order to make best use of the worldwide unique potential of the high-intensity/high-energy proton beam extracted from the Super Proton Synchrotron (SPS) in ECN3. The current status of their study by the CERN Physics Beyond Colliders (PBC) Study Group is presented, including considerations on beam requirements and upgrades, detector R&D and construction, schedules and cost, as well as physics potential within the CERN and worldwide landscape., Comment: 113 pages, 39 figures