Scientific Publications
Degradation of α-MnO2 in Zn-air battery gas-diffusion electrodes: An investigation based on chemical-state mapping
Abstract Here, we present an integrated ultra-high-vacuum (UHV) apparatus for the growth of complex materials and heterostructures. The specific growth technique is the Pulsed Laser Deposition (PLD) by means of a dual-laser source based on an excimer KrF ultraviolet and solid-state Nd:YAG infra-red lasers. By taking advantage of the two laser sources—both lasers can be independently used within the deposition chambers—a large number of different materials—ranging from oxides to metals, to selenides, and others—can be successfully grown in the form of thin films and heterostructures. All of the samples can be in situ transferred between the deposition chambers and the analysis chambers by using vessels and holders’ manipulators. The apparatus also offers the possibility to transfer samples to remote instrumentation under UHV conditions by means of commercially available UHV-suitcases. The dual-PLD operates for in-house research as well as user facility in combination with the Advanced Photo-electric Effect beamline at the Elettra synchrotron radiation facility in Trieste and allows synchrotron-based photo-emission as well as x-ray absorption experiments on pristine films and heterostructures. Authors Benedetto Bozzini, Alessandro Alleva, Valentina Bonanni, Regina Ciancio, George Kourousias, Francesco Guzzi, Piu Rajak, Alessandra Gianoncelli Journal Electrochimica Acta Date 20/12/2024 Consult the paper
Counterfactual rewards promote collective transport using individually controlled swarm microrobots
Abstract Swarm robots offer fascinating opportunities to perform complex tasks beyond the capabilities of individual machines. Just as a swarm of ants collectively moves large objects, similar functions can emerge within a group of robots through individual strategies based on local sensing. However, realizing collective functions with individually controlled microrobots is particularly challenging because of their micrometer size, large number of degrees of freedom, strong thermal noise relative to the propulsion speed, and complex physical coupling between neighboring microrobots. Here, we implemented multiagent reinforcement learning (MARL) to generate a control strategy for up to 200 microrobots whose motions are individually controlled by laser spots. During the learning process, we used so-called counterfactual rewards that automatically assign credit to the individual microrobots, which allows fast and unbiased training. With the help of this efficient reward scheme, swarm microrobots learn to collectively transport a large cargo object to an arbitrary position and orientation, similar to ant swarms. We show that this flexible and versatile swarm robotic system is robust to variations in group size, the presence of malfunctioning units, and environmental noise. In addition, we let the robot swarms manipulate multiple objects simultaneously in a demonstration experiment, highlighting the benefits of distributed control and independent microrobot motion. Control strategies such as ours can potentially enable complex and automated assembly of mobile micromachines, programmable drug delivery capsules, and other advanced lab-on-a-chip applications. Authors Veit-Lorenz Heuthe, Emanuele Panizon, Hongri Gu, and Clemens Bechinger Journal Science Robotics Publication Date 18/12/2024 Consult the publication
Living in Digital Ecosystems: Are We Aware of This?
Abstract In this paper we would like to discuss and evaluate the social and economic impact of contemporary digital ecosystems. We extend the definition of digital ecosystem following the idea of the circular economy based on enhanced and efficient use/reuse of resources and products. We consider an Extended or Circular Digital Ecosystem, a Digital Ecosystem in which the final customers can create value by sharing their data and digital contents through the digital ecosystem services themselves. In this way the data consumers also become data producers. This new concept of a digital ecosystem is closely integrated with the role and actions of the people involved in it, as both users, producers, and managers. A key point of our discussion is the new and very powerful AI algorithms, which are the current focus for research and beyond. We argue that the focus should not only on the algorithms themselves but also be on the data used to train and/or feed such algorithms. We exploit the point of view of data mangers and data curators to address this issue. We make a comparison between the management of scientific research data that is moving towards correct management following the FAIR principles and the data produced every day on the internet by sharing information on social media or any other platform. Since 2009, following the idea of open science, the research community started to focus on the quality of the shared research data. In 2016, the FAIR (Findable, Accessible, Interoperable and Reusable) principles gave the guidelines to share data that are machine interoperable, i.e. that can also be correctly used and interpreted by machines and algorithms. For example, generative AI based on Large Language Models (LLMs), such as the well-known Chat GPT, was trained on a large corpus of text (about 45 TB) to understand and generate text consistently, which consisted mainly of any type of text obtained from web crawling tools. The users of digital ecosystems should be aware that all available data are currently used to train AI algorithms and the results that these algorithms produce depend on the quality of the data used to train them. It is widely demonstrated that database classification by human intervention is prone to biases and errors. Algorithms trained on those datasets will suffer from these biases, resulting in dangerous or inappropriate content. Collecting all these insights, we plead that it is crucial to consider the technical experience of data managers and curators in the wider context of data sharing and management in society, given the immense amount of data produced and available on the network. We also argue that FAIR principles for research data should also be considered in the wider societal context, to avoid potential dangers related to new developments in Artificial Intelligence, which, in our belief and experience, are not harmful in themselves. Our general goal is then to raise awareness on the users of digital ecosystems that the correct sharing of their information can improve the quality of the data available on the network and that the potential dangers of data misuse can be reduced. Authors Stefano Cozzini and Mariarita De Luca Journal Digital Environments and Human Relations Publication Date 10/12/2024 Consult the publication
The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models
Abstract In-context learning (ICL) and supervised fine-tuning (SFT) are two common strategies for improving the performance of modern large language models (LLMs) on specific tasks. Despite their different natures, these strategies often lead to comparable performance gains. However, little is known about whether they induce similar representations inside LLMs. We approach this problem by analyzing the probability landscape of their hidden representations in the two cases. More specifically, we compare how LLMs solve the same question-answering task, finding that ICL and SFT create very different internal structures, in both cases undergoing a sharp transition in the middle of the network. In the first half of the network, ICL shapes interpretable representations hierarchically organized according to their semantic content. In contrast, the probability landscape obtained with SFT is fuzzier and semantically mixed. In the second half of the model, the fine-tuned representations develop probability modes that better encode the identity of answers, while the landscape of ICL representations is characterized by less defined peaks. Our approach reveals the diverse computational strategies developed inside LLMs to solve the same task across different conditions, allowing us to make a step towards designing optimal methods to extract information from language models. Authors Diego Doimo, Alessandro Serra, Alessio Ansuini, Alberto Cazzaniga Journal Neural Information Processing Systems 37 (NeurIPS) 2024 Publication Date 10/12/2024 Consult the publication
Raman Microscopy Identification of Secondary Spurious Phases in Molten GdSr2RuCu2O8-δ Superconductor for Photonics and Plasmonic Applications
Abstract Plasmonic and Photonics applications of superconducting materials, suggested at first by the necessity to minimize the dissipative losses of conventional metals in the high frequency ranges, are topics of growing interest in Optics. In this perspective, GdSr2RuCu2O8-δ (Gd1212) Rutheno-Cuprate Superconductor presents very promising properties, showing both superconducting and magnetically ordered phases coexisting in the same cell. To investigate its features, the fabrication of macroscopic crystallographically oriented samples is necessary. The use of melt texturing techniques has shown to be among the most effective ways to achieve the best characteristics, although the fabrication of high-quality Gd1212 samples is intrinsically difficult. To reach a better understanding of Gd1212 incongruent melting reaction, a series of bulk samples annealed at temperatures below and above the melting temperature was prepared. Raman Microscopy and Mapping performed on molten and re-solidified samples revealed the presence of different phases, corresponding to those identified in our previous studies. These observations were also confirmed by XRD, TGA-DTA, and SEM+EDS characterisations. Secondary phases formation showed a strong dependence on the temperature of the annealing treatments. Susceptibility and magnetization measurements show both superconducting and magnetic transitions and a contribution of different spurious magnetic phases as suggested by EDS. Authors Marcello Gombos, Stefano Managò, Danilo Zola, Giovanni Carapella, Oreste Tarallo, Vito Mocella, Anna Chiara De Luca, Ivo Rendina, Francesco Ruffo, Pasquale Orgiani, Regina Ciancio Journal Accepted at the Annual Meeting of the Association for Computational Linquistics (ACL), Arxiv preprint: 2402.11655 Date 31/10/2024 Consult the paper
Ferritin adsorption onto chrysotile asbestos fibers influences the protein secondary structure
Abstract Asbestos fiber exposure triggers chronic inflammation and cancer. Asbestos fibers can adsorb different types of proteins. The mechanism of this adsorption, not yet completely understood, has been studied in detail mainly with serum albumin and was shown to induce structural changes in the bound protein. The findings of these works regarded mainly the changes of the protein structure, independently of any relation with asbestos-related diseases. For the first time, we have focused our attention to the consequences of the interaction between asbestos fibers and ferritin, a protein involved in iron metabolism, which is strongly modified in asbestos-related diseases. Even if it is known that ferritin can be adsorbed by asbestos fibers, the results of this interaction for the ferritin secondary structure has not previously been studied. One consequence of asbestos-ferritin interaction, is the formation of the so-called ferruginous/asbestos bodies (ABs). In the AB-coating material, the secondary structure of ferritin is modified, and at present, it is unclear whether or not this modification is a direct consequence of the asbestos interaction. In the present study, chrysotile asbestos, more than other asbestos fiber types tested, was found to rapidly bind holo-ferritin, and the presence of iron seemed to play a key role in this process, since iron-free apo-ferritin was adsorbed at a lower level, and iron-saturated chrysotile lost its ferritin-adsorbing capacity. To directly study the details of ferritin adsorption on asbestos fibers, High Resolution Transmission Electron Microscopy (HR-TEM) was employed together with FTIR microspectroscopy and Infrared nanoscopy, which to the best of our knowledge, have not previously been used for this purpose. Chrysotile-bound apo-ferritin underwent a significant change in secondary structure, showing a shift from a prevalent α-helix to a β-sheet conformation. Conversely, the adsorbed holo-ferritin structure appeared to be only weakly modified. These findings add a new potential mechanism to the toxic activities of asbestos: the fibers can modify the structure, and very likely, the function of adsorbed proteins. This, in relation to ferritin, could be a key mechanism in cell iron homeostasis alteration, typically reported in asbestos-related diseases. Authors Martina Zangari, Federica Piccirilli, Lisa Vaccari, Cristian Radu, Paola Zacchi, Annalisa Bernareggi, Sara Leone, Giuliano Zabucchi, Violetta Borelli Journal Helyon Publication date 30/10/2024 Consult the publication
A Comprehensive Analysis of Process Energy Consumption on Multi-Socket Systems with GPUs
Abstract: Robustly estimating energy consumption in High-Performance Computing (HPC) is essential for assessing the energy footprint of modern workloads, particularly in fields such as Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for AI training has heightened concerns about energy consumption and carbon emissions. Existing energy estimation tools often assume exclusive use of computing nodes, a premise that becomes problematic with the advent of supercomputers integrating microservices, as seen in initiatives like Acceleration as a Service (XaaS) and cloud computing. This work investigates the impact of executed instructions on overall power consumption, providing insights into the comprehensive behaviour of HPC systems. We introduce two novel mathematical models to estimate a process’s energy consumption based on the total node energy, process usage, and a normalised vector of the probability distribution of instruction types for CPU and GPU processes. Our approach enables energy accounting for specific processes without the need for isolation. Our models demonstrate high accuracy, predicting CPU power consumption with a mere 1.9% error. For GPU predictions, the models achieve a central relative error of 9.7%, showing a clear tendency to fit the test data accurately. These results pave the way for new tools to measure and account for energy consumption in shared supercomputing environments. Authors: Luis G. Leon-Vega, Niccolò Tosato, Stefano Cozzini Journal: 11th Latin American High Performance Computing Conference, CARLA 2024 Publication date: 30/09/2024 Consult the publication
Effects of In-Air Post Deposition Annealing Process on the Oxygen Vacancy Content in Sputtered GDC Thin Films Probed via Operando XAS and Raman Spectroscopy
Abstract We investigate the ionic mobility in room-temperature RF-sputtered gadolinium doped ceria (GDC) thin films grown on industrial solid oxide fuel cell substrates as a function of the air-annealing at 800 and 1000 °C. The combination of X-ray diffraction, X-ray photoelectron spectroscopy, operando X-ray absorption spectroscopy, and Raman spectroscopy allows us to study the different Ce3+/ Ce4+ ratios induced by the post growth annealing procedure, together with the Ce valence changes induced by different gas atmosphere exposure. Our results give evidence of different kinetics as a function of the annealing temperature, with the sample annealed at 800 °C showing marked changes of the Ce oxidation state when exposed to both reducing and oxidizing gas atmospheres at moderate temperature (300 °C), while the Ce valence is weakly affected for the 1000 °C annealed sample. Raman spectra measurements allow us to trace the responses of the investigated samples to different gas atmospheres on the basis of the presence of different Gd–O bond strengths inside the lattice. These findings provide insight into the microscopic origin of the best performances already observed in SOFCs with a sputtered GDC barrier layer annealed at 800 °C and are fundamental to further improve sputtered GDC thin film performance in energy devices. Authors Nunzia Coppola, Sami Ur Rehman, Giovanni Carapella, Luca Braglia, Vincenzo Vaiano, Dario Montinaro, Veronica Granata, Sandeep Kumar Chaluvadi, Pasquale Orgiani, Piero Torelli, Luigi Maritato, Carmela Aruta, Alice Galdi Journal ACS Applied Electronic Materials Date 25/09/2024 Consult the paper
EfiMon: A Process Analyser for Granular Power Consumption Prediction
Abstract High-performance computing (HPC) and supercomputing are critical in Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for training complex AI models, which can take from days to months, raises significant concerns about energy consumption and carbon emissions. Traditional methods for estimating the energy consumption of HPC workloads rely on metering reports from computing nodes power supply units, assuming exclusive use of the entire node. This assumption is increasingly untenable with the advent of next-generation supercomputers that share resources to accelerate workloads, as seen in initiatives like Acceleration as a Service (XaaS) and cloud computing. This paper introduces EfiMon, an agnostic and non-invasive tool designed to extract detailed information about process execution, including instructions executed within specific time windows and CPU and RAM usage. Additionally, it captures comprehensive system metrics, such as power consumption reported by CPU sockets and PSUs. This data enables the development of prediction models to estimate the energy consumption of individual processes without requiring isolation. Using a regression-based mathematical model, our tool is able to estimate single processes’ power consumption in isolated and shared resource environments. In shared scenarios, the model demonstrates robust performance, deviating by a maximum of 2.2% on Intel-based machines and 4.4% on AMD systems compared to non-shared cases. This significant accuracy showcases EfiMon’s potential for enhancing energy accounting in supercomputing, contributing to more efficient and energy-aware optimisation strategies in HPC. Authors Luis G. Leon-Vega, Niccolò Tosato, Stefano Cozzini Journal Latin American High-Performance Computing Conference (CARLA 2024) Publication Date 25/09/2024 Consult the publication
Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
Abstract N6-Methyladenosine (m6A) is a prevalent RNA post-transcriptional modification that plays crucial roles in RNA stability, structural dynamics, and interactions with proteins. The YT521-B (YTH) family of proteins, which are notable m6A readers, functions through its highly conserved YTH domain. Recent structural investigations and molecular dynamics (MD) simulations have shed light on the mechanism of recognition of m6A by the YTHDC1 protein. Despite advancements, using MD to predict the stabilization induced by m6A on the free energy of binding between RNA and YTH proteins remains challenging due to inaccuracy of the employed force field and limited sampling. For instance, simulations often fail to sufficiently capture the hydration dynamics of the binding pocket. This study addresses these challenges through an innovative methodology that integrates metadynamics, alchemical simulations, and force-field refinement. Importantly, our research identifies hydration of the binding pocket as giving only a minor contribution to the binding free energy and emphasizes the critical importance of precisely tuning force-field parameters to experimental data. By employing a fitting strategy built on alchemical calculations, we refine the m6A partial charge parameters, thereby enabling the simultaneous reproduction of N6 methylation on both the protein binding free energy and the thermodynamic stability of nine RNA duplexes. Our findings underscore the sensitivity of binding free energies to partial charges, highlighting the necessity for thorough parametrization and validation against experimental observations across a range of structural contexts. Authors Valerio Piomponi, Miroslav Krepl, Jiri Sponer, Giovanni Bussi Journal The Journal of Physical Chemistry B, Vol 128, Issue 37 Publication Date 06/09/2024 Consult the paper
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels
Abstract: Time Series Classification (TSC) is essential in fields like medicine, environmental science, and finance, enabling tasks such as disease diagnosis, anomaly detection, and stock price analysis. While machine learning models like Recurrent Neural Networks and InceptionTime are successful in numerous applications, they can face scalability issues due to computational requirements. Recently, ROCKET has emerged as an efficient alternative, achieving state-of-the-art performance and simplifying training by utilizing a large number of randomly generated features from the time series data. However, many of these features are redundant or non-informative, increasing computational load and compromising generalization. Here we introduce Sequential Feature Detachment (SFD) to identify and prune non-essential features in ROCKET-based models, such as ROCKET, MiniRocket, and MultiRocket. SFD estimates feature importance using model coefficients and can handle large feature sets without complex hyperparameter tuning. Testing on the UCR archive shows that SFD can produce models with better test accuracy using only 10% of the original features. We named these pruned models Detach-ROCKET. We also present an end-to-end procedure for determining an optimal balance between the number of features and model accuracy. On the largest binary UCR dataset, Detach-ROCKET improves test accuracy by 0.6% while reducing features by 98.9%. By enabling a significant reduction in model size without sacrificing accuracy, our methodology improves computational efficiency and contributes to model interpretability. We believe that Detach-ROCKET will be a valuable tool for researchers and practitioners working with time series data, who can find a user-friendly implementation of the model at https://github.com/gon-uri/detach_rocket. Authors: Gonzalo Uribarri, Federico Barone, Alessio Ansuini, Eric Fransén Journal: Data Mining and Knowledge Discovery Publication date: 20/08/2024 Consult the publication
Probing conformational dynamics of EGFR mutants via SEIRA spectroscopy: potential implications for tyrosine kinase inhibitor design
Abstract Missense mutations in EGFR’s catalytic domain alter its function, promoting cancer. SEIRA spectroscopy, supported by MD simulations, reveals structural differences in the compactness and hydration of helical motifs between active and inactive EGFR conformations models. These findings provide novel insights into the biophysical mechanisms driving EGFR activation and drug resistance, offering a robust method for studying emerging EGFR mutations and their structural impacts on TKIs efficacy. Authors Emiliano Laudadio, Federica Piccirilli, Henrick Vondracek, Giovanna Mobbili, Marta Stefania Semrau, Paola Storici, Roberta Galeazzi, Elena Romagnoli, Leonardo Sorci, Andrea Toma, Vincenzo Aglieri, Giovanni Birarda, Cristina Minnelli Journal Physical Chemistry Chemical Physics (PCCP) Publication Date 19/08/2024 Consult the publication