Archivio della ricerca - Fondazione Bruno Kessler
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Comparison of Electron Compton Scattering with Positron Compton Scattering in Polyethylene
No full textUnderstanding the interaction of charged particles with polymers is crucial for applications in materials science, radiation physics, and electron spectroscopy. This study investigates the differences in the elastic scattering spectra of electrons and positrons in polyethylene, focusing on the underlying mechanisms that influence the spectral features. The analysis isolates key factors such as recoil energy, Doppler broadening, and the interplay between elastic and inelastic mean free paths. Using Monte Carlo simulations, we analyze the effects of the elastic and inelastic mean free paths on the intensity of the elastic peaks in an energy range from 1000 eV to 3000 eV. The results show that the elastic peaks are consistently more intense for electrons than for positrons, correlating with the differences in the respective elastic scattering cross sections. In addition, we evaluate the effects of different inelastic mean free path models on spectral variations and compare the simulated data showing how variations in inelastic mean free path values affect the intensity of elastic peaks and the elastic reflection coefficient of polyethylene. The percentage difference in the elastic reflection coefficients of electrons and positrons in polyethylene decreases from 49% to 24% when the incident particle energy increases from 1000 eV to 3000 eV. These findings contribute to a refined understanding of the interactions of electrons and positrons with polymers, improve the accuracy of Monte Carlo simulations, and promote methods for material characterization
Sea Ice Semantic Segmentation in Optical Image Based on Adaptive Training Sample Selection and Cross-Attention ResUNet
No full textThe formation of numerous channels among Arctic sea ice provides potential routes for Arctic navigation and the identification and semantic segmentation of sea ice becomes a crucial task. This letter proposes a sea ice semantic segmentation method with adaptive training sample selection and cross-attention mechanism to enhance the robustness under the complex climatic conditions of the Arctic. First, the image is divided into patches. An adaptive iterative clustering on them automatically selects the training samples. Second, ResUNet with a cross-attention mechanism is used for image segmentation. This approach enhances contextual understanding with relatively low computational overhead, enabling better focus on relevant features across different layers of the network. The experimental results demonstrate that the proposed method can achieve high accuracy segmentation with a small training set. Furthermore, the proposed method exhibits segmentation consistency across two datasets and various types of sea ice
Wage expectations and access to healthcare occupations: Evidence from an information experiment
Get PDFWe investigate how correcting students’ wage expectations affects their performance on admission tests for medical and healthcare schools, a critical step for aspiring healthcare professionals. Using a randomized information experiment with Italian applicants, we first elicited their expectations about the starting wage of the healthcare profession they intended to pursue. The treatment group was then informed of the actual starting wages, while the control group received no such information. Finally, we collected and analyzed their test scores. Our findings reveal that applicants with lower wage expectations tend to perform worse on the test. However, correcting these expectations eliminates the performance gap: providing accurate wage information enhances test scores for applicants who initially underestimated wages, while it negatively impacts those who overestimated them
Advanced Modelling and Fabrication of Suspended Silicon Nano-Structures Using Multi-Species Focused Ion Beam Implantation
No full textIn this work, we present recent advances in the development and modelling of silicon nanostructures defined by focused ion beam (FIB) technologies. The development of FIB columns equipped with liquid metal alloy ion sources (LMAIS) opens new opportunities for FIB-based processing, enabling multi-species patterning [1]. Here, we report the use of focused Au+ and Ga+ ion implantation for nanolithography on silicon, focusing on the modelling of the implantation and etching processes to control the thickness and height/depth to the ultimate sub-nm range. Our aim is to develop a predictive model for the etching behavior of FIB-implanted volumes, enabling the design of corrugated and 3D suspended silicon structures with tailored dimensions.
2. Process overview
Previous studies have demonstrated that Ga+ ion implantation in silicon via FIB serves as an efficient and straightforward resistless lithography technique [2-5]. This approach facilitates the fabrication of nanometer-scale structures on silicon and other materials. The implanted Ga+ volume enhances silicon's resistance to both wet chemical etching, enabling its use as an etching hard mask (Figure 1). The resolution of the structures is largely determined by the focused beam's diameter and the ion penetration depth and straggling. Several applications of this method have been reported, including the fabrication of suspended silicon nanowires [6] and single-electron devices [7].
3. Modelling
We model the process to precisely control the vertical dimensions (z-direction) of silicon nanostructures, including both suspended and non-suspended configurations. Process-calibration experiments were conducted for Ga+ using a 30 keV Crossbeam 550L system (Zeiss) and for Au+ using a 35 keV Velion FIB-SEM system (Raith). Fluence values ranged from 1·1014 at/cm2 to 1·1017 at/cm2, avoiding lower doses (insufficient) and higher doses (entering milling regimes) [5]. The implanted samples were etched using tetramethylammonium hydroxide (TMAH) at 25% concentration and 80°C. Atomic force microscopy (AFM) was used to characterize the structures post-implantation and etching.
After calibration, we developed algorithms in Matlab software to predict the etching rates in TMAH (Figure 2) and the final etching depth (Figure 3) for both Au+ and Ga+ ion species as a function of implantation dose. This information is crucial for fine-tuning the dimensions of corrugated and 3D suspended silicon structures. Figure 4 illustrates a corrugated silicon surfaces defined by Ga+ implantation and figure 5 shows suspended nanowires (25-30 nm width, 20 nm thickness) defined by Au+ implantation.
4. Conclusions
In this study, we successfully demonstrated the development and modelling of silicon nanostructures using FIB implantation with both Ga and Au ion species. Through the detailed process-calibration experiments and the development of predictive algorithms, we achieved precise control over the etching behavior of ion-implanted silicon in TMAH, enabling the fabrication of highly defined, suspended 3D nanostructures. Our results show that the combination of FIB implantation and wet etching provides an effective route for creating complex silicon nanostructures, opening new possibilities for the fabrication of nanoscale devices in electronics and sensing
Measurement of photonuclear jet production in ultraperipheral Pb+Pb collisions at √sNN =5.02 TeV with the ATLAS detector
No full textIn ultrarelativistic heavy ion collisions at the LHC, each nucleus acts a sources of high-energy real photons that can scatter off the opposing nucleus in ultraperipheral photonuclear (γ +A) collisions. Hard scattering processes initiated by the photons in such collisions provide a novel method for probing nuclear parton distributions in a kinematic region not easily accessible to other measurements. ATLAS has measured production of dijet and multijet final states in ultraperipheral Pb+Pb collisions at √sNN =5.02 TeV using a dataset recorded in 2018 with an integrated luminosity of 1.72 nb−1. Photonuclear final states are selected by requiring a rapidity gap in the photon direction; this selects events where one of the outgoing nuclei remains intact. Jets are reconstructed using the anti-kt algorithm with radius parameter, R =0.4. Triple-differential cross sections, unfolded for detector response, are measured and presented using two sets of kinematic variables. The first set consists of the total transverse momentum (HT), rapidity, and mass of the jet system. The second set uses HT and particle-level nuclear and photon parton momentum fractions, xA and zγ, respectively. The results are compared with leading-order perturbative QCD calculations of photonuclear jet production cross sections, where all leading order predictions using existing fits fall below the data in the shadowing region. More detailed theoretical comparisons will allow these results to strongly constrain nuclear parton distributions, and these data provide results from the LHC directly comparable to early physics results at the planned Electron-Ion Collider
Seeing the Abstract: Translating the Abstract Language for Vision Language Models
No full textNatural language goes beyond dryly describing visual content. It contains rich abstract concepts to express feeling, creativity and properties that cannot be directly perceived. Yet, current research in Vision Language Models (VLMs) has not shed light on abstract-oriented language. Our research breaks new ground by uncovering its wide presence and under-estimated value, with extensive analysis. Particularly, we focus our investigation on the fashion domain, a highly-representative field with abstract expressions. By analyzing recent large-scale multimodal fashion datasets, we find that abstract terms have a dominant presence, rivaling the concrete ones, providing novel information, and being useful in the retrieval task. However, a critical challenge emerges: current general-purpose or fashion-specific VLMs are pre-trained with databases that lack sufficient abstract words in their text corpora, thus hindering their ability to effectively represent abstract-oriented language. We propose a training-free and model-agnostic method, Abstract-to-Concrete Translator (ACT), to shift abstract representations towards well-represented concrete ones in the VLM latent space, using pre-trained models and existing multi-modal databases. On the text-to-image retrieval task, despite being training-free, ACT outperforms the fine-tuned VLMs in both same- and cross-dataset settings, exhibiting its effectiveness with a strong generalization capability. Moreover, the improvement introduced by ACT is consistent with various VLMs, making it a plug-and-play solution
Following the human thread in social navigation
No full textThe success of collaboration between humans and robots in shared environments relies on the robot's real-time adaptation to human motion. Specifically, in Social Navigation, the agent should be close enough to assist but ready to back up to let the human move freely, avoiding collisions. Human trajectories emerge as crucial cues in Social Navigation, but they are partially observable from the robot's egocentric view and computationally complex to process. We propose the first Social Dynamics Adaptation model (SDA) based on the robot's state-action history to infer the social dynamics. We propose a two-stage Reinforcement Learning framework: the first learns to encode the human trajectories into social dynamics and learns a motion policy conditioned on this encoded information, the current status, and the previous action. Here, the trajectories are fully visible, i.e., assumed as privileged information. In the second stage, the trained policy operates without direct access to trajectories. Instead, the model infers the social dynamics solely from the history of previous actions and statuses in real-time. Tested on the novel Habitat 3.0 platform, SDA sets a novel state of the art (SoA) performance in finding and following humans
Functionality understanding and segmentation in 3D scenes
No full textUnderstanding functionalities in 3D scenes involves interpreting natural language descriptions to locate functional interactive objects, such as handles and buttons, in a 3D environment. Functionality understanding is highly challenging, as it requires both world knowledge to interpret language and spatial perception to identify fine-grained objects. For example, given a task like 'turn on the ceiling light', an embodied AI agent must infer that it needs to locate the light switch, even though the switch is not explicitly mentioned in the task description. To date, no dedicated methods have been developed for this problem. In this paper, we introduce Fun3DU, the first approach designed for functionality understanding in 3D scenes. Fun3DU uses a language model to parse the task description through Chain-of-Thought reasoning in order to identify the object of interest. The identified object is segmented across multiple views of the captured scene by using a vision and language model. The segmentation results from each view are lifted in 3D and aggregated into the point cloud using geometric information. Fun3DU is training-free, relying entirely on pre-trained models. We evaluate Fun3DU on SceneFun3D, the most recent and only dataset to benchmark this task, which comprises over 3000 task descriptions on 230 scenes. Our method significantly outperforms state-of-the-art open-vocabulary 3D segmentation approaches
Predictive Insights for Personalising Esophagogastric Cancer Treatment Process - A Case Study
No full textFor metastatic esophagogastric cancer (EGC), treatments aim to extend survival time, manage symptoms, and enhance the quality of life . However, determining the best treatments for patients with EGC is challenging due to patients’ variability. Personalised treatments supported by predictive models enable tailoring treatment process to individuals. Even so, traditional predictive models often neglect the interaction between treatments, limiting their utility in comprehensive planning. State-of-the-art Predictive Process Monitoring shows promising results in predicting the outcome of the treatment process but often lacks transparency. This paper investigates the potential of supporting healthcare experts in personalising the EGC treatment process, using eXplainable Predictive Process Monitoring methods. A real-world case study among 7,090 patients identifies expert needs for helpful explanations and discusses the capabilities and limitations of existing methods, suggesting future research directions. Our findings demonstrate high-quality explanations with strong fidelity, providing insights validated by expert knowledge. While the resulting explanations are not always actionable, experts acknowledged their value for exploratory analysis