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The ALPHA-g experiment at CERN aims to precisely measure the terrestrial gravitational acceleration of antihydrogen atoms. A radial Time Projection Chamber (rTPC), that surrounds the ALPHA-g magnetic trap, is employed to determine the annihilation location, called the vertex. The standard approach requires identifying the trajectories of the ionizing particles in the rTPC from the location of their interaction in the gas (spacepoints), and inferring the vertex positions by finding the point where those trajectories (helices) pass closest to one another. In this work, we present a novel approach to vertex reconstruction using an ensemble of models based on the PointNet deep learning architecture. The newly developed model, PointNet Ensemble for Annihilation Reconstruction (PEAR), directly learns the relation between the location of the vertices and the rTPC spacepoints, thus eliminating the need to identify and fit the particle tracks. PEAR shows strong performance in reconstructing vertical vertex positions from simulated data, that is superior to the standard approach for all metrics considered. Furthermore, the deep learning approach can reconstruct the vertical vertex position when the standard approach fails.

Petri Nets (PN) are widely used for modeling concurrent and distributed systems, but face challenges in modeling adaptive systems. To address this, we have formalized "rewritable" PT nets (RwPT) using Maude, a declarative language with sound rewriting logic semantics. Recently, we introduced a modular approach that utilizes algebraic operators to construct large RwPT models. This technique employs composite node labeling to outline symmetries in hierarchical organization, preserved through net rewrites. Once stochastic parameters are added to the formalism, we present an automated process to derive a lumped CTMC from the quotient graph generated by an RwPT., Comment: In Proceedings ICLP 2024, arXiv:2502.08453

An automated temperature-controlled electrical DC voltage and DC resistance multiple reference standard (MRS) has been developed by Measurements International (MI) with the scientific support from the Istituto Nazionale di Ricerca Metrologica (INRIM). The MRS includes a 10 V, a 1 {\Omega}, and a 10 k{\Omega} standards selectable via a switch unit. This setup allows the artifact calibration of high-end calibrators and multimeters used in low-frequency electrical measurements. The two resistors are high-stability standards from MI, while the 10 V standard is based on a low-noise circuit developed by INRIM in collaboration with MI. A key innovation is the internal real-time clock calendar, which displays the calibration values of the MRS standards and their updated values internally calculated. This ensures reliable use of the MRS standards over extended periods between calibrations, effectively minimizing uncertainties due to their drift. The standards are housed in a thermal box, minimizing temperature variations. The MRS standards meet the uncertainty requirements defined by calibrators and multimeters manufacturers for artifact calibration and can also serve as laboratory references or travelling standards for interlaboratory comparisons (ILCs). MI is currently commercializing the MRS., Comment: 8 pages 6 figures

Despite its importance, studying economic behavior across diverse, non-WEIRD (Western, Educated, Industrialized, Rich, and Democratic) populations presents significant challenges. We address this issue by introducing a novel methodology that uses Large Language Models (LLMs) to create synthetic cultural agents (SCAs) representing these populations. We subject these SCAs to classic behavioral experiments, including the dictator and ultimatum games. Our results demonstrate substantial cross-cultural variability in experimental behavior. Notably, for populations with available data, SCAs' behaviors qualitatively resemble those of real human subjects. For unstudied populations, our method can generate novel, testable hypotheses about economic behavior. By integrating AI into experimental economics, this approach offers an effective and ethical method to pilot experiments and refine protocols for hard-to-reach populations. Our study provides a new tool for cross-cultural economic studies and demonstrates how LLMs can help experimental behavioral research.

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.

The ALPHA-g experiment at CERN aims to perform the first-ever direct measurement of the effect of gravity on antimatter, determining its weight to within 1% precision. This measurement requires an accurate prediction of the vertical position of annihilations within the detector. In this work, we present a novel approach to annihilation position reconstruction using an ensemble of models based on the PointNet deep learning architecture. The newly developed model, PointNet Ensemble for Annihilation Reconstruction (PEAR) outperforms the standard approach to annihilation position reconstruction, providing more than twice the resolution while maintaining a similarly low bias. This work may also offer insights for similar efforts applying deep learning to experiments that require high resolution and low bias., Comment: 6 pages, 4 figures, submitted to Machine Learning and the Physical Sciences Workshop at the 38th conference on Neural Information Processing Systems (NeurIPS)

In this contribution we extend the concept of a Petri net morphism to Elementary Object Systems (EOS). EOS are a nets-within-nets formalism, i.e. we allow the tokens of a Petri net to be Petri nets again. This nested structure has the consequence that even systems defined by very small Petri nets have a quite huge reachability graph. In this contribution we use automorphism to describe symmetries of the Petri net topology. Since these symmetries carry over to markings as well this leads to a condensed state space, too.

Petri Nets (PN) are extensively used as a robust formalism to model concurrent and distributed systems; however, they encounter difficulties in accurately modeling adaptive systems. To address this issue, we defined rewritable PT nets (RwPT) using Maude, a declarative language that ensures consistent rewriting logic semantics. Recently, we proposed a modular approach that employs algebraic operators to build extensive RwPT models. This methodology uses composite node labeling to maintain hierarchical organization through net rewrites and has been shown to be effective. Once stochastic parameters are integrated into the formalism, we introduce an automated procedure to derive a lumped CTMC from the quotient graph generated by a modular RwPT model. To demonstrate the effectiveness of our method, we present a fault-tolerant manufacturing system as a case study., Comment: In Proceedings FROM 2024, arXiv:2410.23020

Accurate characterization of quantum yield is crucial to the reconstruction of energy depositions in silicon at the eV scale. This work presents a new method for experimentally calculating quantum yield using vacuum UV-sensitive silicon photomultipliers (SiPMs), which can be used to determine the probabilities that a UV photon absorbed in a silicon crystal will produce one, two, or three electron-hole pairs. Results are presented which fully constrain the distribution at photon energies up to 7.75eV. This method works by exploiting the saturation of photon detection efficiency which occurs when these devices are biased sufficiently high above their avalanche breakdown voltage. The measured quantum yield values are lower than those that have been previously reported by experimental data and modelling -- this is expected to impact the sensitivity of experiments searching for light dark matter through direct detection in semiconductors, and should also be taken into account when characterizing the performance of UV photodetectors with high quantum efficiency. Measurements have been taken using a Hamamatsu VUV4 and an FBK VUV-HD3 device, showing good agreements between devices, and at a range of temperatures from 163-233K. The validity of the method is assessed using supplementary measurements of absolute photon detection efficiency, and an additional novel method of measuring average quantum yield using DC current-voltage measurements of SiPMs is presented and used for corroboration., Comment: 13 pages, 11 figures

Violence and armed conflicts have emerged as prominent factors driving food crises. However, the extent of their impact remains largely unexplored. This paper provides an in-depth analysis of the impact of violent conflicts on food security in Africa. We performed a comprehensive correlation analysis using data from the Famine Early Warning Systems Network (FEWSNET) and the Armed Conflict Location Event Data (ACLED). Our results show that using conflict data to train machine learning models leads to a 1.5% increase in accuracy compared to models that do not incorporate conflict-related information. The key contribution of this study is the quantitative analysis of the impact of conflicts on food security predictions.

Reports from the Famine Early Warning Systems Network (FEWSNET) serve as the benchmark for food security predictions which is crucial for stakeholders in planning interventions and support people in need. This paper assesses the predictive accuracy of FEWSNET's food security forecasting, by comparing predictions to the following ground truth assessments at the administrative boundaries-livelihood level, revealing an overall high accuracy of 78\% across diverse timeframes and locations. However, our analysis also shows significant variations in performance across distinct regions and prediction periods. Therefore, our analysis sheds light on strengths, weaknesses, and areas for improvement in the context of food security predictions. The insights derived from this study not only enhance our understanding of FEWSNET's capabilities but also emphasize the importance of continuous refinement in forecasting methodologies.

In modern society, service robots are increasingly recognized for their wide range of practical applications. In large and crowded social spaces, such as museums and hospitals, these robots are required to safely move in the environment while exhibiting user-friendly behavior. Ensuring the safe and socially acceptable operation of robots in such settings presents several challenges. To enhance the social acceptance in the design process of service robots, we present a systematic analysis of requirements, categorized into functional and non-functional. These requirements are further classified into different categories, with a single requirement potentially belonging to multiple categories. Finally, considering the specific case of a receptionist robotic agent, we discuss the requirements it should possess to ensure social acceptance.

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

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)

AI regulations are expected to prohibit machine learning models from using sensitive attributes during training. However, the latest Natural Language Processing (NLP) classifiers, which rely on deep learning, operate as black-box systems, complicating the detection and remediation of such misuse. Traditional bias mitigation methods in NLP aim for comparable performance across different groups based on attributes like gender or race but fail to address the underlying issue of reliance on protected attributes. To partly fix that, we introduce NLPGuard, a framework for mitigating the reliance on protected attributes in NLP classifiers. NLPGuard takes an unlabeled dataset, an existing NLP classifier, and its training data as input, producing a modified training dataset that significantly reduces dependence on protected attributes without compromising accuracy. NLPGuard is applied to three classification tasks: identifying toxic language, sentiment analysis, and occupation classification. Our evaluation shows that current NLP classifiers heavily depend on protected attributes, with up to $23\%$ of the most predictive words associated with these attributes. However, NLPGuard effectively reduces this reliance by up to $79\%$, while slightly improving accuracy., Comment: Paper accepted at CSCW 2024

Experiments aimed at direct searches for WIMP dark matter require highly effective reduction of backgrounds and control of any residual radioactive contamination. In particular, neutrons interacting with atomic nuclei represent an important class of backgrounds due to the expected similarity of a WIMP-nucleon interaction, so that such experiments often feature a dedicated neutron detector surrounding the active target volume. In the context of the development of DarkSide-20k detector at INFN Gran Sasso National Laboratory (LNGS), several R&D projects were conceived and developed for the creation of a new hybrid material rich in both hydrogen and gadolinium nuclei to be employed as an essential element of the neutron detector. Thanks to its very high cross-section for neutron capture, gadolinium is one of the most widely used elements in neutron detectors, while the hydrogen-rich material is instrumental in efficiently moderating the neutrons. In this paper results from one of the R&Ds are presented. In this effort the new hybrid material was obtained as a poly(methyl methacrylate) (PMMA) matrix, loaded with gadolinium oxide in the form of nanoparticles. We describe its realization, including all phases of design, purification, construction, characterization, and determination of mechanical properties of the new material.

Large-area next-generation physics experiments rely on using Silicon Photo-Multiplier (SiPM) devices to detect single photons, which trigger charge avalanches. The noise mechanism of external cross-talk occurs when secondary photons produced during a charge avalanche escape from an SiPM and trigger other devices within a detector system. This work presents measured spectra of the secondary photons emitted from the Hamamatsu VUV4 and Fondazione Bruno Kessler VUV-HD3 SiPMs stimulated by laser light, near operational voltages. The work describes the Microscope for the Injection and Emission of Light (MIEL) setup, which is an experimental apparatus constructed for this purpose. Measurements have been performed at a range of over-voltage values and temperatures from 86~K to 293~K. The number of photons produced per avalanche at the source are calculated from the measured spectra and determined to be 40$\pm$9 and 61$\pm$11 photons produced per avalanche for the VUV4 and VUV-HD3 respectively at 4 volts over-voltage. No significant temperature dependence is observed within the measurement uncertainties. The overall number of photons emitted per avalanche from each SiPM device are also reported., Comment: 16 pages, 14 figures

We show that the manifold of quantum states is endowed with a rich and nontrivial geometric structure. We derive the Fubini-Study metric of the projective Hilbert space of a multi-qubit quantum system, endowing it with a Riemannian metric structure, and investigate its deep link with the entanglement of the states of this space. As a measure, we adopt the Entanglement Distance E preliminary proposed in [1]. Our analysis shows that entanglement has a geometric interpretation: E(|psi>) is the minimum value of the sum of the squared distances between |psi> and its conjugate states, namely the states v^mu . sigma^mu |psi>, where v^mu are unit vectors and mu runs on the number of parties. We derive a general method to determine when two states are not the same state up to the action of local unitary operators. We prove that the entanglement distance, along with its convex roof expansion to mixed states, fulfills the three conditions required for an entanglement measure: that is i) E(|psi>) =0 iff |psi> is fully separable; ii) E is invariant under local unitary transformations; iii) E doesn't increase under local operation and classical communications. Two different proofs are provided for this latter property. We also show that in the case of two qubits pure states, the entanglement distance for a state |psi> coincides with two times the square of the concurrence of this state. We propose a generalization of the entanglement distance to continuous variable systems. Finally, we apply the proposed geometric approach to the study of the entanglement magnitude and the equivalence classes properties, of three families of states linked to the Greenberger-Horne-Zeilinger states, the Briegel Raussendorf states and the W states. As an example of an application for the case of a system with continuous variables, we have considered a system of two coupled Glauber coherent states.

The direct search for dark matter in the form of weakly interacting massive particles (WIMP) is performed by detecting nuclear recoils (NR) produced in a target material from the WIMP elastic scattering. A promising experimental strategy for direct dark matter search employs argon dual-phase time projection chambers (TPC). One of the advantages of the TPC is the capability to detect both the scintillation and charge signals produced by NRs. Furthermore, the existence of a drift electric field in the TPC breaks the rotational symmetry: the angle between the drift field and the momentum of the recoiling nucleus can potentially affect the charge recombination probability in liquid argon and then the relative balance between the two signal channels. This fact could make the detector sensitive to the directionality of the WIMP-induced signal, enabling unmistakable annual and daily modulation signatures for future searches aiming for discovery. The Recoil Directionality (ReD) experiment was designed to probe for such directional sensitivity. The TPC of ReD was irradiated with neutrons at the INFN Laboratori Nazionali del Sud, and data were taken with 72 keV NRs of known recoil directions. The direction-dependent liquid argon charge recombination model by Cataudella et al. was adopted and a likelihood statistical analysis was performed, which gave no indications of significant dependence of the detector response to the recoil direction. The aspect ratio R of the initial ionization cloud is estimated to be 1.037 +/- 0.027 and the upper limit is R < 1.072 with 90% confidence level, Comment: 20 pages, 10 figures, submitted to Eur. Phys. J. C

Generative AI tools such as chatGPT are poised to change the way people engage with online information. Recently, Microsoft announced their "new Bing" search system which incorporates chat and generative AI technology from OpenAI. Google has announced plans to deploy search interfaces that incorporate similar types of technology. These new technologies will transform how people can search for information. The research presented here is an early investigation into how people make use of a generative AI chat system (referred to simply as chat from here on) as part of a search process, and how the incorporation of chat systems with existing search tools may effect users search behaviors and strategies. We report on an exploratory user study with 10 participants who used a combined Chat+Search system that utilized the OpenAI GPT-3.5 API and the Bing Web Search v5 API. Participants completed three search tasks. In this pre-print paper of preliminary results, we report on ways that users integrated AI chat into their search process, things they liked and disliked about the chat system, their trust in the chat responses, and their mental models of how the chat system generated responses.

To the best of the authors' knowledge, this work presents the first large-scale indoor experimental assessment of an implementation of the emerging Smart ElectroMagnetic Environment (SEME) paradigm, which is based on the deployment of static-passive EM skins (SP-EMSs) to enhance the coverage in a 5 [GHz] Wi-Fi network. Unlike standard (laboratory-based) validations reported in the state-of-the-art (SoA) literature, the scenario at hand mimics a realistic indoor environment to replicate as close as possible the user experience when using commodity devices. Representative results from the experimental field trials are re-ported to confirm the performance predictions arising from the numerical studies and the tolerance analyses carried out with a commercial ray-tracing (RT) tool. Besides experimentally validating the SEME idea, this study is also aimed at (roughly) quantifying the economic advantage of a SEME implementation, relying on simple-manufacturing/low-cost field manipulating devices without any additional biasing circuitry, with respect to standard approaches that imply the densification of the active radiating sources.

Transformers' compute-intensive operations pose enormous challenges for their deployment in resource-constrained EdgeAI / tinyML devices. As an established neural network compression technique, quantization reduces the hardware computational and memory resources. In particular, fixed-point quantization is desirable to ease the computations using lightweight blocks, like adders and multipliers, of the underlying hardware. However, deploying fully-quantized Transformers on existing general-purpose hardware, generic AI accelerators, or specialized architectures for Transformers with floating-point units might be infeasible and/or inefficient. Towards this, we propose SwiftTron, an efficient specialized hardware accelerator designed for Quantized Transformers. SwiftTron supports the execution of different types of Transformers' operations (like Attention, Softmax, GELU, and Layer Normalization) and accounts for diverse scaling factors to perform correct computations. We synthesize the complete SwiftTron architecture in a $65$ nm CMOS technology with the ASIC design flow. Our Accelerator executes the RoBERTa-base model in 1.83 ns, while consuming 33.64 mW power, and occupying an area of 273 mm^2. To ease the reproducibility, the RTL of our SwiftTron architecture is released at https://github.com/albertomarchisio/SwiftTron., Comment: To appear at the 2023 International Joint Conference on Neural Networks (IJCNN), Queensland, Australia, June 2023

Coupling regular topologies with optimised routing algorithms is key in pushing the performance of interconnection networks of supercomputers.In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalised Fat-Trees (PGFTs) which minimises congestion risk even under massive network degradation caused by equipment failure.Dmodc computes forwarding tables with a closed-form arithmetic formula by relying on a fast preprocessing phase.This allows complete re-routing of networks with tens of thousands of nodes in less than a second.In turn, this greatly helps centralised fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters., Comment: arXiv admin note: text overlap with arXiv:2211.11817

Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalized Fat-Trees (PGFTs) which minimizes congestion risk even under massive topology degradation caused by equipment failure. It applies a modulo-based computation of forwarding tables among switches closer to the destination, using only knowledge of subtrees for pre-modulo division. Dmodc allows complete rerouting of topologies with tens of thousands of nodes in less than a second, which greatly helps centralized fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters. We compare Dmodc against routing algorithms available in the InfiniBand control software (OpenSM) first for routing execution time to show feasibility at scale, and then for congestion risk under degradation to demonstrate robustness. The latter comparison is done using static analysis of routing tables under random permutation (RP), shift permutation (SP) and all-to-all (A2A) traffic patterns. Results for Dmodc show A2A and RP congestion risks similar under heavy degradation as the most stable algorithms compared, and near-optimal SP congestion risk up to 1% of random degradation.

The ALPHA Collaboration, based at the CERN Antiproton Decelerator, has recently implemented a novel beamline for low-energy ($\lesssim$ 100 eV) positron and antiproton transport between cylindrical Penning traps that have strong axial magnetic fields. Here, we describe how a combination of semianalytical and numerical calculations were used to optimise the layout and design of this beamline. Using experimental measurements taken during the initial commissioning of the instrument, we evaluate its performance and validate the models used for its development. By combining data from a range of sources, we show that the beamline has a high transfer efficiency, and estimate that the percentage of particles captured in the experiments from each bunch is (78 $\pm$ 3)% for up to $10^{5}$ antiprotons, and (71 $\pm$ 5)% for bunches of up to $10^{7}$ positrons., Comment: 15 pages, 15 figures

Dark matter lighter than 10 GeV/c$^2$ encompasses a promising range of candidates. A conceptual design for a new detector, DarkSide-LowMass, is presented, based on the DarkSide-50 detector and progress toward DarkSide-20k, optimized for a low-threshold electron-counting measurement. Sensitivity to light dark matter is explored for various potential energy thresholds and background rates. These studies show that DarkSide-LowMass can achieve sensitivity to light dark matter down to the solar neutrino floor for GeV-scale masses and significant sensitivity down to 10 MeV/c$^2$ considering the Migdal effect or interactions with electrons. Requirements for optimizing the detector's sensitivity are explored, as are potential sensitivity gains from modeling and mitigating spurious electron backgrounds that may dominate the signal at the lowest energies.

There are strong incentives to build models that demonstrate outstanding predictive performance on various datasets and benchmarks. We believe these incentives risk a narrow focus on models and on the performance metrics used to evaluate and compare them -- resulting in a growing body of literature to evaluate and compare metrics. This paper strives for a more balanced perspective on classifier performance metrics by highlighting their distributions under different models of uncertainty and showing how this uncertainty can easily eclipse differences in the empirical performance of classifiers. We begin by emphasising the fundamentally discrete nature of empirical confusion matrices and show how binary matrices can be meaningfully represented in a three dimensional compositional lattice, whose cross-sections form the basis of the space of receiver operating characteristic (ROC) curves. We develop equations, animations and interactive visualisations of the contours of performance metrics within (and beyond) this ROC space, showing how some are affected by class imbalance. We provide interactive visualisations that show the discrete posterior predictive probability mass functions of true and false positive rates in ROC space, and how these relate to uncertainty in performance metrics such as Balanced Accuracy (BA) and the Matthews Correlation Coefficient (MCC). Our hope is that these insights and visualisations will raise greater awareness of the substantial uncertainty in performance metric estimates that can arise when classifiers are evaluated on empirical datasets and benchmarks, and that classification model performance claims should be tempered by this understanding., Comment: 60 pages, 45 figures

Past research has attributed the online circulation of misinformation to two main factors - individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithm-mediated information diffusion) - and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called "The Big Sort"). We hypothesized that this latter factor matters for the online spreading of misinformation not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an "hype machine" for misinformation (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects) but worked as a supply-and-demand system: misinformation news items scaled linearly with the number of users in each state (with a scaling exponent beta=1, and a goodness of fit R2 = 0.95). Furthermore, deviations from such a universal pattern were best explained by state-level personality and cultural factors (R2 = {0.12, 0.39}), rather than socioeconomic conditions (R2 = {0.15, 0.29}) or, as one would expect, political characteristics (R2 ={0.06, 0.21}). Higher-than-expected circulation of any type of news (including reputable news) was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness).

It is hard to establish whether a company supports internal sustainability efforts (ISEs) like gender equality, diversity, and general staff welfare, not least because of lack of methodologies operationalizing these internal sustainability practices, and of data honestly documenting such efforts. We developed and validated a six-dimension framework reflecting Internal Sustainability Efforts (ISEs), gathered more than 350K employee reviews of 104 major companies across the whole US for the (2008-2020) years, and developed a deep-learning framework scoring these reviews in terms of the six ISEs. Commitment to ISEs manifested itself at micro-level -- companies scoring high in ISEs enjoyed high stock growth. This new conceptualization of ISEs offers both theoretical implications for the literature in corporate sustainability, and practical implications for companies and policymakers. To further explore these implications, researchers need to add potentially missing ISEs, to do so for more companies, and establish the causal relationship between company success and ISEs., Comment: 9 pages + 15 pages of appendix, to appear in Humanities & Social Sciences Communications

Petri Nets (PN) are a central, theoretically sound model for concurrent or distributed systems but, at least in their classical definition, not expressive enough to represent dynamic reconfiguration capabilities. On the other side, Rewriting Logic has proved to be a natural semantic framework for several formal models of concurrent/distributed systems. We propose a compact, efficient Maude formalization of dynamically reconfigurable PT nets (with inhibitor arcs), using as a running example the specification of a simple, fault-tolerant manufacturing system. We discuss the advantages of such a combined approach, as well as some concerns that it raises., Comment: In Proceedings AppFM 2021, arXiv:2111.07538

Randomized controlled trials (RCTs) are the gold standard for assessing drug safety and efficacy. However, RCTs have some drawbacks which have led to the use of single-arm studies to make certain internal drug development and regulatory decisions, particularly in oncology. Hybrid controlled trials with real-world data (RWD), in which the control arm is composed of both trial and real-world patients, have the potential to help address some of the shortcomings of both RCTs and single-arm studies in particular situations, such as when a disease has low prevalence or when the standard of care to be used in the control arm is ineffective or highly toxic and an experimental therapy shows early promise. This paper discusses why it may be beneficial to consider hybrid controlled trials with RWD, what such a design entails, when it may be appropriate, and how to conduct the analyses. We propose a novel two-step borrowing method for the construction of hybrid control arms. We use simulations to demonstrate the operating characteristics of dynamic and static borrowing methods, and highlight the trade-offs and analytic decisions that study teams will need to address when designing a hybrid study., Comment: 71 pgs (with supplemental) 3 Tables, 4 Figures

The ALPHA collaboration has successfully demonstrated the production and the confinement of cold antihydrogen, $\overline{\mathrm{H}}$. An analysis of trapping data allowed a stringent limit to be placed on the electric charge of the simplest antiatom. Charge neutrality of matter is known to a very high precision, hence a neutrality limit of $\overline{\mathrm{H}}$ provides a test of CPT invariance. The experimental technique is based on the measurement of the deflection of putatively charged $\overline{\mathrm{H}}$ in an electric field. The tendency for trapped $\overline{\mathrm{H}}$ atoms to be displaced by electrostatic fields is measured and compared to the results of a detailed simulation of $\overline{\mathrm{H}}$ dynamics in the trap. An extensive survey of the systematic errors is performed, with particular attention to those due to the silicon vertex detector, which is the device used to determine the $\overline{\mathrm{H}}$ annihilation position. The limit obtained on the charge of the $\overline{\mathrm{H}}$ atom is \mbox{$ Q = (-1.3\pm1.8\pm0.4)\times10^{-8}$}, representing the first precision measurement with $\overline{\mathrm{H}}$., Comment: 5 pages, 3 figures

Personal Information Management (PIM) refers to the practice and the study of the activities a person performs in order to acquire or create, store, organize, maintain, retrieve, use, and distribute information in each of its many forms (paper and digital, in e-mails, files, Web pages, text messages, tweets, posts, etc.) as needed to meet life's many goals (everyday and long-term, work-related and not) and to fulfill life's many roles and responsibilities (as parent, spouse, friend, employee, member of community, etc.). PIM activities are an effort to establish, use, and maintain a mapping between information and need. Activities of finding (and re-finding) move from a current need toward information while activities of keeping move from encountered information toward anticipated need. Meta-level activities such as maintaining, organizing, and managing the flow of information focus on the mapping itself. Tools and techniques of PIM can promote information integration with benefits for each kind of PIM activity and across the life cycle of personal information. Understanding how best to accomplish this integration without inadvertently creating problems along the way is a key challenge of PIM., Comment: Final version available at https://www.routledgehandbooks.com/doi/10.1081/E-ELIS4-120053695

We propose an innovative method to shift the transversal position of the focal point of an optical cavity keeping it actively stabilized. Our cavity is a 4 mirrors bow-tie cavity and the spatial shift of the resonant mode is obtained by properly rotating the two curved mirrors by piezo actuators. This method allows us to move the transversal position of the cavity focal point of $135 \mu m$ in a time of $50 ms$, keeping the resonance condition of the cavity by means of the Pound-Drever-Hall technique. We propose to use this technique for the generation of 2-color X-rays via Inverse Compton Scattering (ICS). This technique exploits the large average power stored in the high finesse cavity by shifting the laser beam with respect to the electron beam trajectory, hence controlling the spatial superposition of the electron and photon beams in the interaction region. Arranging two cavities assembled one on top of the other, with different collision angle with the electron beam, allows the generation of X-ray bursts of different energies just by swiftly moving the two cavities, switching the two focal points onto the electron beam trajectory, thus activating in sequence two different ICS spectral lines.

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute- and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them., Comment: Accepted for publication in IEEE Access

Multiple Sclerosis (MS) is a chronic and potentially highly disabling disease that can cause permanent damage and deterioration of the central nervous system. In Europe it is the leading cause of non-traumatic disabilities in young adults, since more than 700,000 EU people suffer from MS. Although recent studies on MS pathophysiology have been provided, MS remains a challenging disease. In this context, thanks to recent advances in software and hardware technologies, computational models and computer simulations are becoming appealing research tools to support scientists in the study of such disease. Thus, motivated by this consideration we propose in this paper a new model to study the evolution of MS in silico, and the effects of the administration of Daclizumab drug, taking into account also spatiality and temporality of the involved phenomena. Moreover, we show how the intrinsic symmetries of the system can be exploited to drastically reduce the complexity of its analysis., Comment: Submitted to CIBB 2019 post proceedings - LNCS

How long antihydrogen atoms linger in the ALPHA magnetic trap is an important characteristic of the ALPHA apparatus. The initial trapping experiments in 2010 [1] were conducted with 38 detected antiatoms confined for 172 ms and in 2011 [2] with seven for 1000 s. Long confinement times are necessary to perform detailed frequency scans during spectroscopic measurements. An analysis carried out, using machine learning methods, on more than 1000 antiatoms confined for several hours in the ALPHA-2 magnetic trap, yields a preliminary lower limit to the lifetime of 66 hours. Hence this observation suggests that the measured confinement time of antihydrogen is extended by more than two orders of magnitude. [1] Andresen, G. B. et al. (ALPHA collaboration), Nature 468, 673-676 (2010) [2] Andresen, G. B. et al. (ALPHA collaboration), Nature Phys. 7, 558-564 (2011)

Airbnb is one of the most successful examples of sharing economy marketplaces. With rapid and global market penetration, understanding its attractiveness and evolving growth opportunities is key to plan business decision making. There is an ongoing debate, for example, about whether Airbnb is a hospitality service that fosters social exchanges between hosts and guests, as the sharing economy manifesto originally stated, or whether it is (or is evolving into being) a purely business transaction platform, the way hotels have traditionally operated. To answer these questions, we propose a novel market analysis approach that exploits customers' reviews. Key to the approach is a method that combines thematic analysis and machine learning to inductively develop a custom dictionary for guests' reviews. Based on this dictionary, we then use quantitative linguistic analysis on a corpus of 3.2 million reviews collected in 6 different cities, and illustrate how to answer a variety of market research questions, at fine levels of temporal, thematic, user and spatial granularity, such as (i) how the business vs social dichotomy is evolving over the years, (ii) what exact words within such top-level categories are evolving, (iii) whether such trends vary across different user segments and (iv) in different neighbourhoods., Comment: 17 pages, 8 figures, Proceedings of The Web Conference 2020

Presented here is the design of the Mechanical Ventilator Milano (MVM), a novel mechanical ventilator designed for rapid mass production in response to the COVID-19 pandemic to address the urgent shortage of intensive therapy ventilators in many countries, and the growing difficulty in procuring these devices through normal supply chains across borders. This ventilator is an electro-mechanical equivalent of the old and reliable Manley Ventilator, and is able to operate in both pressure-controlled and pressure-supported ventilation modes. MVM is optimized for the COVID-19 emergency, thanks to the collaboration with medical doctors in the front line. MVM is designed for large-scale production in a short amount of time and at a limited cost, as it relays on off-the-shelf components, readily available worldwide. Operation of the MVM requires only a source of compressed oxygen (or compressed medical air) and electrical power. Initial tests of a prototype device with a breathing simulator are also presented. Further tests and developments are underway. At this stage the MVM is not yet a certified medical device but certification is in progress.

Characterizing the movement patterns of animals is crucial to improve our understanding of their behavior and thus develop adequate conservation strategies. Such investigations, which could not have been implemented in practice only a few years ago, have been facilitated through the recent advances in tracking methods that enable researchers to study animal movement at an unprecedented spatio-temporal resolution. However, the identification and extraction of patterns from spatio-temporal trajectories is still a general problem that has relevance for many applications. Here, we rely on the concept of resting event networks to identify the presence of daily mobility patterns in animal spatio-temporal trajectories. We illustrate our approach by analyzing spatio-temporal trajectories of several fish species in a large hydropeaking river., Comment: 10 pages, 6 figures + Appendix

Graph transformation systems (GTS) have been successfully proposed as a general, theoretically sound model for concurrency. Petri nets (PN), on the other side, are a central and intuitive formalism for concurrent or distributed systems, well supported by a number of analysis techniques/tools. Some PN classes have been shown to be instances of GTS. In this paper, we change perspective presenting an operational semantics of GTS in terms of Symmetric Nets, a well-known class of Coloured Petri nets featuring a structured syntax that outlines model symmetries. Some practical exploitations of the proposed operational semantics are discussed. In particular, a recently developed structural calculus for SN is used to validate graph rewriting rules in a symbolic way., Comment: In Proceedings FROM 2019, arXiv:1909.00584

Since 2006 a small group of researchers from the Italian National Institute for Nuclear Physics started to realized one of the first European Researchers' Night in Europe: a one night-event, supported by the European Commission, that falls every last Friday of September to promote the researcher's figure and its work. Today, after thirteen editions, the project has evolved by involving more than 60 scientific partners and more than 400 events/year spread from the North to the South of Italy in 30 cities, captivating more than 50.000 attendees with a not negligible impact on the people and the press. During the years, the project has followed and sometimes anticipated the science communication trend, and BEES (BE a citizEn Scientist) is the last step of this long and thrilling evolution that brought to a huge public engagement in our territory. The experience, the methodology, and the major successful examples of the organized events are presented together with the results of the long term project impact., Comment: Presented ad EDULEARN19 11th annual International Conference on Education and New Learning Technologies Palma de Mallorca (Spain). 1st - 3rd of July, 2019

We address the use of optical parametric oscillator (OPO) to counteract phase-noise in quantum optical communication channels, and demonstrate reduction of phase diffusion for coherent signals travelling through a suitably tuned OPO. In particular, we theoretically and experimentally show that there is a threshold value on the phase-noise, above which OPO can be exploited to squeeze phase noise. The threshold depends on the energy of the input coherent state, and on the relevant parameters of the OPO, i.e. gain and input/output and crystal loss rates.

Since twelve years the researchers of the Roman area organize the European Researchers' Night, a project funded by European Commission to discover science and meet researchers through an Europe-wide public event dedicated to fun learning. Since the first edition in 2006, when the National Laboratory of INFN's in Frascati hosted 4000 visitors the project is always grown up to 50,000 attendees and more than 50 scientific partners. In the last edition, The Frascati Scienza association, which was born to coordinate the event, operated in over 30 Italian cities from south to north of the peninsula. In addition, the Made in Science project - European Research Week 2016/17 - has been the one of the largest project funded by the European Commission and it's often referred to as a model for organization and communication to the general public. The 12 yeas data collected and results obtained, as well as some of the most important experiences in public communication of science will be shown., Comment: 12th International Technology, Education and Development Conference, 5-7 March, 2018, Valencia, Spain, Page 1772-1780

How diverse are sharing economy platforms? Are they fair marketplaces, where all participants operate on a level playing field, or are they large-scale online aggregators of offline human biases? Often portrayed as easy-to-access digital spaces whose participants receive equal opportunities, such platforms have recently come under fire due to reports of discriminatory behaviours among their users, and have been associated with gentrification phenomena that exacerbate preexisting inequalities along racial lines. In this paper, we focus on the Airbnb sharing economy platform, and analyse the diversity of its user base across five large cities. We find it to be predominantly young, female, and white. Notably, we find this to be true even in cities with a diverse racial composition. We then introduce a method based on the statistical analysis of networks to quantify behaviours of homophily, heterophily and avoidance between Airbnb hosts and guests. Depending on cities and property types, we do find signals of such behaviours relating both to race and gender. We use these findings to provide platform design recommendations, aimed at exposing and possibly reducing the biases we detect, in support of a more inclusive growth of sharing economy platforms., Comment: 17 pages, 1 figure

We introduce Cloud4IoT, a platform offering automatic deployment, orchestration and dynamic configuration of IoT support software components and data-intensive applications for data processing and analytics, thus enabling plug-and-play integration of new sensor objects and dynamic workload scalability. Cloud4IoT enables the concept of Infrastructure as Code in the IoT context: it empowers IoT operations with the flexibility and elasticity of Cloud services. Furthermore it shifts traditionally centralized Cloud architectures towards a more distributed and decentralized computation paradigm, as required by IoT technologies, bridging the gap between Cloud Computing and IoT ecosystems. Thus, Cloud4IoT is playing a role similar to the one covered by solutions like Fog Computing, Cloudlets or Mobile Edge Cloud. The hierarchical architecture of Cloud4IoThosts a central Cloud platform and multiple remote edge Cloud modules supporting dedicated devices, namely the IoT Gateways, through which new sensor objects are made accessible to the platform. Overall, the platform is designed in order to support systems where IoT-based and data intensive applications may pose specific requirements for low latency, restricted available bandwidth, or data locality. Cloud4IoT is built on several Open Source technologies for containerisation and implementations of standards, protocols and services for the IoT. We present the implementation of the platform and demonstrate it in two different use cases., Comment: The article has been accepted for publication by IEEE

The in-beam tests of two Si pixel type TRACE detectors have been performed at Laboratori Nazionali di Legnaro (Italy). The aim was to investigate the possibility of identifying heavy-ion reactions products with mass A~10 at low kinetic energy, i.e., around 10 MeV. Two separate read-out chains, digital and analog, were used. The Pulse Shape Analysis technique was employed to obtain the identification matrices for the digitally processed part of the data. Separation in both charge and mass was obtained, however, the $\alpha$ particles contaminated significantly the recorded data in the lower energy part. Due to this effect, the identification of the light products ($^{7,6}$Li isotopes) could be possible down only to ~20 MeV, Comment: 6 pages, 4 figures

At National Institute of Metrological Research (INRIM), an automated setup to calibrate DC Voltage generators, mainly top-level calibrators from 1 mV to 1 kV has been developed. The heart of the setup is an INRIM-built automated fixed ratios DC Voltage divider. The significant achievement of this setup is the possibility to interconnect the divider, a DMM characterized in linearity, a DC Voltage Standard and a DC Voltage generator under calibration and automatically to manage the calibration process. This calibration method allows to save a lot of time, to improve the reliability and to increase the accuracy of the calibration of generators. The relative uncertainties of the system span from 0.6x10-6 to 1.2x10-4 improving the previous capabilities of the INRIM laboratory for calibration of programmable multifunction instruments. In addition, this system allows to avoid the employment of several Standards (some of them still manually operating) carrying out the entire process without changing the setup configuration and without the presence of operators. The concept of this setup can be transferred to secondary high-level electrical calibration Laboratories that could be consider it useful for their calibration activities., Comment: 6 pages 8 figures

In this paper, we investigate whether text from a Community Question Answering (QA) platform can be used to predict and describe real-world attributes. We experiment with predicting a wide range of 62 demographic attributes for neighbourhoods of London. We use the text from QA platform of Yahoo! Answers and compare our results to the ones obtained from Twitter microblogs. Outcomes show that the correlation between the predicted demographic attributes using text from Yahoo! Answers discussions and the observed demographic attributes can reach an average Pearson correlation coefficient of \r{ho} = 0.54, slightly higher than the predictions obtained using Twitter data. Our qualitative analysis indicates that there is semantic relatedness between the highest correlated terms extracted from both datasets and their relative demographic attributes. Furthermore, the correlations highlight the different natures of the information contained in Yahoo! Answers and Twitter. While the former seems to offer a more encyclopedic content, the latter provides information related to the current sociocultural aspects or phenomena., Comment: Submitted to ICWSM2017

The gravitational interaction of antimatter and matter has never been directly probed. ALPHA-g is a novel experiment that aims to perform the first measurement of the antihydrogen gravitational mass. A fundamental requirement for this new apparatus is a position sensitive particle detector around the antihydrogen trap which provides information about antihydrogen annihilation location. The proposed detector is a radial Time Projection Chamber, or \textit{rTPC}, whose concept is being developed at TRIUMF. A simulation of the detector and the development of the reconstruction software, used to determine the antihydrogen annihilation point, is presented alongside with the expected performance of the rTPC., Comment: 6 pages, 4 figures, LEAP2016