Search

Given a graph $G=(V(G), E(G))$ and a set $P\subseteq V(G)$, the following concepts have been recently introduced: $(i)$ two elements of $P$ are \emph{mutually visible} if there is a shortest path between them without further elements of $P$; $(ii)$ $P$ is a \emph{mutual-visibility set} if its elements are pairwise mutually visible; $(iii)$ the \emph{mutual-visibility number} of $G$ is the size of any largest mutual-visibility set. % In this work we continue to investigate about these concepts. We first focus on mutual-visibility in Cartesian products. For this purpose, too, we introduce and investigate independent mutual-visibility sets. In the very special case of the Cartesian product of two complete graphs the problem is shown to be equivalent to the well-known Zarenkiewicz's problem. We also characterize the triangle-free graphs with the mutual-visibility number equal to $3$., Comment: 17 pages, 3 figures

We have studied the scintillation of pure and doped sodium iodide crystals handled in low humidity conditions under external $\alpha$ and $\gamma$ excitation from room temperature down to 4K. The light yield of pure sodium iodide was seen to increase at low temperatures by a factor 30 compared to room temperature, up to a maximum of 40 photons/keV under $\gamma$ excitation at 60K, stabilizing to 30 photons/keV at lower temperatures. Thallium doped sodium iodide fluctuates by 20% around the room temperature value, stabilizing at 40 photons/keV at low temperature. $\alpha / \gamma$ quenching factor stabilizes at roughly 0.7 for both materials beneath 50K. Time constants of both materials slow greatly at low temperature, reaching tens of microseconds. Cryogenic applications of these materials are complexified by their mechanical fragility and hygroscopicity., Comment: 16 pages, 8 figures

A novel online distillation technique was developed for the XENON1T dark matter experiment to reduce intrinsic background components more volatile than xenon, such as krypton or argon, while the detector was operating. The method is based on a continuous purification of the gaseous volume of the detector system using the XENON1T cryogenic distillation column. A krypton-in-xenon concentration of $(360 \pm 60)$ ppq was achieved. It is the lowest concentration measured in the fiducial volume of an operating dark matter detector to date. A model was developed and fit to the data to describe the krypton evolution in the liquid and gas volumes of the detector system for several operation modes over the time span of 550 days, including the commissioning and science runs of XENON1T. The online distillation was also successfully applied to remove Ar-37 after its injection for a low energy calibration in XENON1T. This makes the usage of Ar-37 as a regular calibration source possible in the future. The online distillation can be applied to next-generation experiments to remove krypton prior to, or during, any science run. The model developed here allows further optimization of the distillation strategy for future large scale detectors.

Delayed single- and few-electron emissions plague dual-phase time projection chambers, limiting their potential to search for light-mass dark matter. This paper examines the origins of these events in the XENON1T experiment. Characterization of the intensity of delayed electron backgrounds shows that the resulting emissions are correlated, in time and position, with high-energy events and can effectively be vetoed. In this work we extend previous S2-only analyses down to a single electron. From this analysis, after removing the correlated backgrounds, we observe rates < 30 events/(electron*kg*day) in the region of interest spanning 1 to 5 electrons. We derive 90% confidence upper limits for dark matter-electron scattering, first direct limits on the electric dipole, magnetic dipole, and anapole interactions, and bosonic dark matter models, where we exclude new parameter space for dark photons and solar dark photons., Comment: 20 pages, 17 figures, Updated to correct published Solar Dark Photon limit

Several particle-physics experiments use poly(methyl methacrylate) (a.k.a. PMMA or acrylic) vessels to contain liquid scintillators. Superluminal charged particles emitted from radioactive impurities in or near the acrylic can emit Cherenkov radiation in the ultraviolet (UV) spectra range. If acrylic fluoresces in the visible range due to this UV light, it could be a source of background in experiments where the main signal is visible scintillation light, or UV scintillation light that is absorbed and re-emitted at visible wavelengths by a wavelength shifter. Some of these experiments operate at low temperature. The fluorescence of these materials could change with temperature so we have studied the fluorescence of the acrylic used in the DEAP-3600 experiment down to a temperature of 4 K, and compared it to the common wavelength shifter 1,1,4,4-tetraphenyl-1,3-butadiene (TPB). The light yield and wavelength spectra of these materials were characterized by exciting the sample with 285 nm UV light which acted as a proxy for Cherenkov light in the detector. Spectral measurements indicate at least part of the fluorescence of the acrylic is due to additives. Time-resolved measurements show the light yields of our acrylic sample, TPB sample, and the relative light between both samples, all increase when cooling down. At room temperature, the light yield of our acrylic sample relative to the TPB sample is 0.3 %, while it reaches 0.5 % at 4 K. The main fluorescence time constant of the acrylic is less than a few nanoseconds., Comment: 15 pages, 12 figures

The selection of low-radioactive construction materials is of the utmost importance for rare-event searches and thus critical to the XENONnT experiment. Results of an extensive radioassay program are reported, in which material samples have been screened with gamma-ray spectroscopy, mass spectrometry, and $^{222}$Rn emanation measurements. Furthermore, the cleanliness procedures applied to remove or mitigate surface contamination of detector materials are described. Screening results, used as inputs for a XENONnT Monte Carlo simulation, predict a reduction of materials background ($\sim$17%) with respect to its predecessor XENON1T. Through radon emanation measurements, the expected $^{222}$Rn activity concentration in XENONnT is determined to be 4.2$\,(^{+0.5}_{-0.7})\,\mu$Bq/kg, a factor three lower with respect to XENON1T. This radon concentration will be further suppressed by means of the novel radon distillation system.

We study the interaction of a two-level atom with a single-mode nonlinear electromagnetic resonator, considering coupling strengths ranging from zero to the so-called deep strong coupling regime. When the qubit-resonator coupling is very strong, the standard Kerr model for the resonator becomes questionable. Moreover, recently, it has been shown that extra care is needed when constructing gauge-independent theories in the presence of approximations as the truncation of the Hilbert space of the matter system. Such a truncation can ruin gauge invariance leading to non-physical results, especially when the light-matter interactions strength is very high. Here we face and solve this issues to provide a consistent nonlinear-resonator quantum Rabi model satisfying the gauge principle., Comment: 10 pages, 4 figures

We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile phones. Our approach relies on a continuous formulation that enables to estimate a refined disparity map at any arbitrary output resolution. Thereby, it can handle effectively the unbalanced camera setup typical of nowadays mobile phones, which feature both high and low resolution RGB sensors within the same device. Moreover, our neural network can process seamlessly the output of a variety of stereo methods and, by refining the disparity maps computed by a traditional matching algorithm like SGM, it can achieve unpaired zero-shot generalization performance compared to state-of-the-art end-to-end stereo models., Comment: 3DV 2021 Oral paper. Project page: https://cvlab-unibo.github.io/neural-disparity-refinement-web

Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. Reliance on multi-task learning to align features across domains has been the standard way to tackle it. In this paper, we take a different path and propose RefRec, the first approach to investigate pseudo-labels and self-training in UDA for point clouds. We present two main innovations to make self-training effective on 3D data: i) refinement of noisy pseudo-labels by matching shape descriptors that are learned by the unsupervised task of shape reconstruction on both domains; ii) a novel self-training protocol that learns domain-specific decision boundaries and reduces the negative impact of mislabelled target samples and in-domain intra-class variability. RefRec sets the new state of the art in both standard benchmarks used to test UDA for point cloud classification, showcasing the effectiveness of self-training for this important problem., Comment: 3DV 2021 (Oral) Code: https://github.com/CVLAB-Unibo/RefRec

In liquid argon-based particle detectors, slow wavelength shifters (WLSs) could be used alongside the common, nanosecond scale, WLS tetraphenyl butadiene (TPB) for background mitigation purposes. At room temperature, pyrene has a moderate fluorescence light yield (LY) and a time constant of the order of hundreds of nanoseconds. In this work, four pyrene-doped polystyrene films with various purities and concentrations were characterized in terms of LY and decay time constants in a range of temperature between 4 K and 300 K under ultraviolet excitation. These films were found to have a LY between 35 and 50% of that of evaporated TPB. All light yields increase when cooling down, while the decays slow down. At room temperature, we observed that pyrene purity is strongly correlated with emission lifetime: highest obtainable purity samples were dominated by decays with emission time constants of $\sim$ 250-280 ns, and lower purity samples were dominated by an $\sim$ 80 ns component. One sample was investigated further to better understand the monomer and excimer emissions of pyrene. The excimer-over-monomer intensity ratio decreases when the temperature goes down, with the monomer emission dominating below $\sim$ 87 K., Comment: 22 pages, 14 figures

Although recent semantic segmentation methods have made remarkable progress, they still rely on large amounts of annotated training data, which are often infeasible to collect in the autonomous driving scenario. Previous works usually tackle this issue with Unsupervised Domain Adaptation (UDA), which entails training a network on synthetic images and applying the model to real ones while minimizing the discrepancy between the two domains. Yet, these techniques do not consider additional information that may be obtained from other tasks. Differently, we propose to exploit self-supervised monocular depth estimation to improve UDA for semantic segmentation. On one hand, we deploy depth to realize a plug-in component which can inject complementary geometric cues into any existing UDA method. We further rely on depth to generate a large and varied set of samples to Self-Train the final model. Our whole proposal allows for achieving state-of-the-art performance (58.8 mIoU) in the GTA5->CS benchmark benchmark. Code is available at https://github.com/CVLAB-Unibo/d4-dbst., Comment: Accepted at WACV 2022

Although deep neural networks have achieved remarkable results for the task of semantic segmentation, they usually fail to generalize towards new domains, especially when performing synthetic-to-real adaptation. Such domain shift is particularly noticeable along class boundaries, invalidating one of the main goals of semantic segmentation that consists in obtaining sharp segmentation masks. In this work, we specifically address this core problem in the context of Unsupervised Domain Adaptation and present a novel low-level adaptation strategy that allows us to obtain sharp predictions. Moreover, inspired by recent self-training techniques, we introduce an effective data augmentation that alleviates the noise typically present at semantic boundaries when employing pseudo-labels for self-training. Our contributions can be easily integrated into other popular adaptation frameworks, and extensive experiments show that they effectively improve performance along class boundaries., Comment: Accepted at WACV 2022

We describe a technique, applicable to liquid-argon-based dark matter detectors, allowing for discrimination of alpha-decays in detector regions with incomplete light collection from nuclear-recoil-like events. Nuclear recoils and alpha events preferentially excite the liquid argon (LAr) singlet state, which has a decay time of ~6 ns. The wavelength-shifter TPB, which is typically applied to the inside of the active detector volume to make the LAr scintillation photons visible, has a short re-emission time that preserves the LAr scintillation timing. We developed a wavelength-shifting polymeric film - pyrene-doped polystyrene - for the DEAP-3600 detector and describe the production method and characterization. At liquid argon temperature, the film's re-emission timing is dominated by a modified exponential decay with time constant of 279(14) ns and has a wavelength-shifting efficiency of 46.4(2.9) % relative to TPB, measured at room temperature. By coating the detector neck (a region outside the active volume where the scintillation light collection efficiency is low) with this film, the visible energy and the scintillation pulse shape of alpha events in the neck region are modified, and we predict that through pulse shape discrimination, the coating will afford a suppression factor of O($10^{5}$) against these events., Comment: 18 pages,10 figures, Submitted to NIMA

Dark matter particles with Planck-scale mass ($\simeq10^{19}\text{GeV}/c^2$) arise in well-motivated theories and could be produced by several cosmological mechanisms. Using a blind analysis of data collected over a 813 d live time with DEAP-3600, a 3.3 t single-phase liquid argon-based dark matter experiment at SNOLAB, a search for supermassive dark matter was performed, looking for multiple-scatter signals. No candidate signal events were observed, leading to the first direct detection constraints on Planck-scale mass dark matter. Leading limits constrain dark matter masses between $8.3\times10^{6}$ and $1.2\times10^{19} \text{GeV}/c^2$, and cross sections for scattering on $^{40}$Ar between $1.0\times10^{-23}$ and $2.4\times10^{-18} \text{cm}^2$. These are used to constrain two composite dark matter models.

The community may be on the verge of detecting low-frequency gravitational waves from massive black hole binaries (MBHBs), but no examples of binary active galactic nuclei (AGN) have been confirmed. Because MBHBs are intrinsically rare, the most promising detection methods utilize photometric data from all-sky surveys. Recently D'Orazio & Di Stefano 2018 (arXiv:1707.02335) suggested gravitational self-lensing as a method of detecting AGN in close separation binaries. In this study we calculate the detectability of lensing signatures in realistic populations of simulated MBHBs. Within our model assumptions, we find that VRO's LSST should be able to detect 10s to 100s of self-lensing binaries, with the rate uncertainty depending primarily on the orientation of AGN disks relative to their binary orbits. Roughly a quarter of lensing detectable systems should also show detectable Doppler boosting signatures. If AGN disks tend to be aligned with the orbit, lensing signatures are very nearly achromatic, while in misaligned configurations the bluer optical bands are lensed more than redder ones. Whether substantial obscuring material (e.g.~a dusty torus) will be present in close binaries remains uncertain, but our estimates suggest that a substantial fraction of systems would still be observable in this case., Comment: 10+3 pages, 4+2 figures, comments welcome and appreciated

We present a catalogue containing 839 candidate post common envelope systems. Common envelope evolution is very important in stellar astrophysics, particularly in the context of very compact and short-period binaries, including cataclysmic variables, as progenitors of e.g. supernovae type Ia or mergers of black holes and/or neutron stars. At the same time it is a barely understood process in binary evolution. Due to limitations, since partially remedied, on direct simulation, early investigations were mainly focused on providing analytic prescriptions of the outcome of common envelope evolution. In recent years, detailed hydrodynamical calculations have produced deeper insight into the previously elusive process of envelope ejection. However, a direct link between observations and theory of this relatively short-lived phase in binary evolution has not been forthcoming. Therefore, the main insight to be gained from observations has to be derived from the current state of systems likely to have gone through a common envelope. Here we present an extensive catalogue of such observations as found in the literature. The aim of this paper is to provide a reliable set of data, obtained from observations, to be used in the theoretical modelling of common envelope evolution. In this catalogue, the former common envelope donor star is commonly observed as a white dwarf star or as a hot sub-dwarf star. This catalogue includes period and mass estimates, wherever obtainable. Some binaries are border line cases to allow an investigation of the transition between a common envelope formation and other mass-transfer processes., Comment: 29(+23) pages, 6(+4) figures, 1(+2) tables. Accepted for publication in APJ

Electrical energy consumption data accessibility for low voltage end users is one of the pillars of smart grids. In some countries, despite the presence of smart meters, a fragmentary data availability and/or the lack of standardization hinders the creation of post-metering value-added services and confines such innovative solutions to the prototypal and experimental level. We take inspiration from the technology adopted in Italy, where the national regulatory authority actively supported the definition of a solution agreed upon by all the involved stakeholders. In this context, smart meters are enabled to convey data to low voltage end users through a power line communication channel (CHAIN 2) in near real-time. The aim of this paper is twofold. On the one hand, it describes the proof of concept that the channel underwent and its subsequent validation (with performances nearing 99% success rate). On the other hand, it defines a classification framework (I2MA) for post-metering value-added services, in order to categorize each use case based on both level of service and expected benefits, and understand its maturity level. As an example, we apply the methodology to the 16 use cases defined in Italy. The lessons learned from the regulatory, technological, and functional approach of the Italian experience bring us to the provision of recommendations for researchers and industry experts. In particular, we argue that a well-functioning post-metering value-added services' market can flourish when: i) distribution system operators certify the measurements coming from smart meters; ii) national regulatory authorities support the technological innovation needed for setting up this market; and iii) service providers create customer-oriented solutions based on smart meters' data.

We suggest a new experiment sensitive to a possible difference between the amount of CP violation as measured on the surface of the Earth and in a lower gravity environment. Our proposed experiment is model independent and could yield a $5\sigma$ measurement within tens of days, indicating a dependence of the level of CP violation in the neutral kaon system on the local gravitational potential., Comment: Contribution to the 2021 Gravitation session of the 55th Rencontres de Moriond

Let $G=(V,E)$ be a graph and $P\subseteq V$ a set of points. Two points are mutually visible if there is a shortest path between them without further points. $P$ is a mutual-visibility set if its points are pairwise mutually visible. The mutual-visibility number of $G$ is the size of any largest mutual-visibility set. In this paper we start the study about this new invariant and the mutual-visibility sets in undirected graphs. We introduce the mutual-visibility problem which asks to find a mutual-visibility set with a size larger than a given number. We show that this problem is NP-complete, whereas, to check whether a given set of points is a mutual-visibility set is solvable in polynomial time. Then we study mutual-visibility sets and mutual-visibility numbers on special classes of graphs, such as block graphs, trees, grids, tori, complete bipartite graphs, cographs. We also provide some relations of the mutual-visibility number of a graph with other invariants., Comment: Added comparisons with general position set and general position number concepts. Results achieved in the first version are still valid, but sometime refined. Added new references. 23 pages, 6 figures

Thermal annealing plays a crucial role for healing the defectiveness in the ion implanted regions of DIMOSFETs (Double Implanted MOSFETs) devices. In this work, we have studied the effect of a double step annealing on the body (Al implanted) and the source (P implanted) regions of such devices. We found that a high temperature annealing (1750{\deg}C, 1h) followed by a lower temperature one (1500{\deg}C, 4h) is mandatory to achieve low defects concentration and good crystal quality in both the n- and p- type zones of the device.

Equilibrium phase transitions between a normal and a photon condensate state (also known as superradiant phase transitions) are a highly debated research topic, where proposals for their occurrence and no-go theorems have chased each other for the past four decades. Recent no-go theorems have demonstrated that gauge invariance forbids second-order phase transitions to a photon condensate state when the cavity-photon mode is assumed to be {\it spatially uniform}. However, it has been theoretically predicted that a collection of three-level systems coupled to light can display a first-order phase transition to a photon condensate state. %{It has also been recently shown that truncation of the Hilbert space of the matter system can affect the gauge invariance of the theory. However, it is always possible to obtain approximate Hamiltonians obeying the gauge principle in the truncated Hilbert space.} Here, we demonstrate a general no-go theorem valid also for truncated, gauge-invariant models which forbid first-order as well as second-order superradiant phase transitions in the absence of a coupling with a magnetic field. In particular, we explicitly consider the cases of interacting electrons in a lattice and $M$-level systems., Comment: 12 pages, 1 figure

In this work, we have studied the crystal defectiveness and doping activation subsequent to ion implantation and post-annealing by using various techniques including photoluminescence (PL), Raman spectroscopy and transmission electron microscopy (TEM). The aim of this work was to test the effectiveness of double step annealing to reduce the density of point defects generated during the annealing of a P implanted 4H-SiC epitaxial layer. The outcome of this work evidences that neither the first, 1 hour isochronal annealing at 1650 - 1700 - 1750 {\deg}C, nor the second one, at 1500 {\deg}C for times between 4 hour and 14 hour, were able to recover a satisfactory crystallinity of the sample and achieve dopant activations exceeding 1%.

The Muon g-2 Experiment at Fermi National Accelerator Laboratory (FNAL) has measured the muon anomalous precession frequency $\omega_a$ to an uncertainty of 434 parts per billion (ppb), statistical, and 56 ppb, systematic, with data collected in four storage ring configurations during its first physics run in 2018. When combined with a precision measurement of the magnetic field of the experiment's muon storage ring, the precession frequency measurement determines a muon magnetic anomaly of $a_{\mu}({\rm FNAL}) = 116\,592\,040(54) \times 10^{-11}$ (0.46 ppm). This article describes the multiple techniques employed in the reconstruction, analysis and fitting of the data to measure the precession frequency. It also presents the averaging of the results from the eleven separate determinations of \omega_a, and the systematic uncertainties on the result., Comment: 29 pages, 19 figures. Published in Physical Review D

The Fermi National Accelerator Laboratory has measured the anomalous precession frequency $a^{}_\mu = (g^{}_\mu-2)/2$ of the muon to a combined precision of 0.46 parts per million with data collected during its first physics run in 2018. This paper documents the measurement of the magnetic field in the muon storage ring. The magnetic field is monitored by nuclear magnetic resonance systems and calibrated in terms of the equivalent proton spin precession frequency in a spherical water sample at 34.7$^\circ$C. The measured field is weighted by the muon distribution resulting in $\tilde{\omega}'^{}_p$, the denominator in the ratio $\omega^{}_a$/$\tilde{\omega}'^{}_p$ that together with known fundamental constants yields $a^{}_\mu$. The reported uncertainty on $\tilde{\omega}'^{}_p$ for the Run-1 data set is 114 ppb consisting of uncertainty contributions from frequency extraction, calibration, mapping, tracking, and averaging of 56 ppb, and contributions from fast transient fields of 99 ppb., Comment: Added one citation and corrected missing normalization in Eqs (35) and (36)

We present the first results of the Fermilab Muon g-2 Experiment for the positive muon magnetic anomaly $a_\mu \equiv (g_\mu-2)/2$. The anomaly is determined from the precision measurements of two angular frequencies. Intensity variation of high-energy positrons from muon decays directly encodes the difference frequency $\omega_a$ between the spin-precession and cyclotron frequencies for polarized muons in a magnetic storage ring. The storage ring magnetic field is measured using nuclear magnetic resonance probes calibrated in terms of the equivalent proton spin precession frequency ${\tilde{\omega}'^{}_p}$ in a spherical water sample at 34.7$^{\circ}$C. The ratio $\omega_a / {\tilde{\omega}'^{}_p}$, together with known fundamental constants, determines $a_\mu({\rm FNAL}) = 116\,592\,040(54)\times 10^{-11}$ (0.46\,ppm). The result is 3.3 standard deviations greater than the standard model prediction and is in excellent agreement with the previous Brookhaven National Laboratory (BNL) E821 measurement. After combination with previous measurements of both $\mu^+$ and $\mu^-$, the new experimental average of $a_\mu({\rm Exp}) = 116\,592\,061(41)\times 10^{-11}$ (0.35\,ppm) increases the tension between experiment and theory to 4.2 standard deviations, Comment: 10 pages; 4 figures

This paper presents the beam dynamics systematic corrections and their uncertainties for the Run-1 data set of the Fermilab Muon g-2 Experiment. Two corrections to the measured muon precession frequency $\omega_a^m$ are associated with well-known effects owing to the use of electrostatic quadrupole (ESQ) vertical focusing in the storage ring. An average vertically oriented motional magnetic field is felt by relativistic muons passing transversely through the radial electric field components created by the ESQ system. The correction depends on the stored momentum distribution and the tunes of the ring, which has relatively weak vertical focusing. Vertical betatron motions imply that the muons do not orbit the ring in a plane exactly orthogonal to the vertical magnetic field direction. A correction is necessary to account for an average pitch angle associated with their trajectories. A third small correction is necessary because muons that escape the ring during the storage time are slightly biased in initial spin phase compared to the parent distribution. Finally, because two high-voltage resistors in the ESQ network had longer than designed RC time constants, the vertical and horizontal centroids and envelopes of the stored muon beam drifted slightly, but coherently, during each storage ring fill. This led to the discovery of an important phase-acceptance relationship that requires a correction. The sum of the corrections to $\omega_a^m$ is 0.50 $\pm$ 0.09 ppm; the uncertainty is small compared to the 0.43 ppm statistical precision of $\omega_a^m$., Comment: 35 pages, 29 figures. Accepted by Phys. Rev. Accel. Beams

The DEAP-3600 detector searches for the scintillation signal from dark matter particles scattering on a 3.3 tonne liquid argon target. The largest background comes from $^{39}$Ar beta decays and is suppressed using pulseshape discrimination (PSD). We use two types of PSD algorithm: the prompt-fraction, which considers the fraction of the scintillation signal in a narrow and a wide time window around the event peak, and the log-likelihood-ratio, which compares the observed photon arrival times to a signal and a background model. We furthermore use two algorithms to determine the number of photons detected at a given time: (1) simply dividing the charge of each PMT pulse by the charge of a single photoelectron, and (2) a likelihood analysis that considers the probability to detect a certain number of photons at a given time, based on a model for the scintillation pulseshape and for afterpulsing in the light detectors. The prompt-fraction performs approximately as well as the log-likelihood-ratio PSD algorithm if the photon detection times are not biased by detector effects. We explain this result using a model for the information carried by scintillation photons as a function of the time when they are detected., Comment: 14 pages, 9 figures

In the last decade, wildfires have become wider and more destructive. The climate change and the growth of urban areas may further increase the probability of incidence of large-scale fires. The risk of fire can be lowered with preventive measures. Among them, firefighting lines are used to stop the fire from spreading beyond them. Due to high costs of installation and maintenance, their placement must be carefully planned. In this work, we address the wildfire management problem from a theoretical point of view and define a risk function to model the fire diffusion phenomena. The land is modeled by a mixed graph in which vertices are areas subject to fire with a certain probability while edges model the probability of fire spreading from one area to another. To reduce the risk, we introduce the {\sc Windy Firebreak Location} problem that addresses the optimal positioning of firefighting lines under budget constraints. We study the complexity of the problem and prove its hardness even when the graph is planar, bipartite, with maximum degree four and the propagation probabilities are equal to one. We also show an efficient polynomial time algorithm for particular instances on trees., Comment: 37 pages, 10 figures, 2 algorithms

We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, associated with the COVID-19 pandemic by looking at the excess mortality trends of an area that registered one of the highest death tolls in the world during the first wave, Northern Italy. We do not find any evidence of a sizable COVID-19 harvesting effect, neither in the summer months after the slowdown of the first wave nor at the beginning of the second wave. According to our estimates, only a minor share of the total excess deaths detected in Northern Italian municipalities over the entire period under scrutiny (February - November 2020) can be attributed to an anticipatory role of COVID-19. A slightly higher share is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect can only account for less than 20% of excess deaths. Furthermore, the lower mortality rates observed in these areas at the beginning of the second wave may be due to several factors other than a harvesting effect, including behavioral change and some degree of temporary herd immunity. The very limited presence of short-run mortality displacement restates the case for containment policies aimed at minimizing the health impacts of the pandemic.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification., Comment: 11 pages, 1 table

A quantum dipole interacting with an optical cavity is one of the key models in cavity quantum electrodynamics (cavity-QED). To treat this system theoretically, the typical approach is to truncate the dipole to two levels. However, it has been shown that in the ultrastrong-coupling regime, this truncation naively destroys gauge invariance. By truncating in a manner consistent with the gauge principle, we introduce master equations {for open systems} to compute gauge-invariant emission spectra, photon flux rates, and quantum correlation functions which show significant disagreement with previous results obtained using the standard quantum Rabi model. Explicit examples are shown using both the dipole gauge and the Coulomb gauge., Comment: 21 pages, 9 figures

Under the paradigm of Edge Computing (EC), a Network Operator (NO) deploys computational resources at the network edge and let third-party Service Providers (SPs) run on top of them, as tenants. Besides the clear advantages for SPs and final users thanks to the vicinity of computation nodes, a NO aims to allocate edge resources in order to increase its own utility, including bandwidth saving, operational cost reduction, QoE for its users, etc. However, while the number of third-party services competing for edge resources is expected to dramatically grow, the resources deployed cannot increase accordingly, due to physical limitations. Therefore, smart strategies are needed to fully exploit the potential of EC, despite its constrains. To this aim, we propose to leverage service adaptability, a dimension that has mainly been neglected so far: each service can adapt to the amount of resources that the NO has allocated to it, balancing the fraction of service computation performed at the edge and relying on remote servers, e.g., in the Cloud, for the rest. We propose EdgeMORE, a resource allocation strategy in which SPs express their capabilities to adapt to different resource constraints, by declaring the different configurations under which they are able to run, specifying the resources needed and the utility provided to the NO. The NO then chooses the most convenient option per each SP, in order to maximize the total utility. We formalize EdgeMORE as a Integer Linear Program. We show via simulation that EdgeMORE greatly improves EC utility with respect to the standard where no multiple options for running services are allowed.

COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at identifying automatically lung parenchyma and lobes. Next, we combined such segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the obtained classification results with those obtained by three expert radiologists on a dataset consisting of 162 CT scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for COVID-19 detection, outperforming those yielded by the expert radiologists, and an average lesion categorization accuracy of over 84%. Results also show that a significant role is played by prior lung and lobe segmentation that allowed us to enhance performance by over 20 percent points. The interpretation of the trained AI models, moreover, reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai.

Motivation: Hi-C matrices are cornerstones for qualitative and quantitative studies of genome folding, from its territorial organization to compartments and topological domains. The high dynamic range of genomic distances probed in Hi-C assays reflects in an inherent stochastic background of the interactions matrices, which inevitably convolve the features of interest with largely aspecific ones. Results: Here we introduce a discuss essHi-C, a method to isolate the specific, or essential component of Hi-C matrices from the aspecific portion of the spectrum that is compatible with random matrices. Systematic comparisons show that essHi-C improves the clarity of the interaction patterns, enhances the robustness against sequencing depth, allows the unsupervised clustering of experiments in different cell lines and recovers the cell-cycle phasing of single-cells based on Hi-C data. Thus, essHi-C provides means for isolating significant biological and physical features from Hi-C matrices., Comment: 14 pages, 4 figures. This is the Authors' Original Version of the article, which has been accepted for publication in Bioinformatics published by Oxford University Press

The Aria project consists of a plant, hosting a 350 m cryogenic isotopic distillation column, the tallest ever built, which is currently in the installation phase in a mine shaft at Carbosulcis S.p.A., Nuraxi-Figus (SU), Italy. Aria is one of the pillars of the argon dark-matter search experimental program, lead by the Global Argon Dark Matter Collaboration. Aria was designed to reduce the isotopic abundance of $^{39}$Ar, a $\beta$-emitter of cosmogenic origin, whose activity poses background and pile-up concerns in the detectors, in the argon used for the dark-matter searches, the so-called Underground Argon (UAr). In this paper, we discuss the requirements, design, construction, tests, and projected performance of the plant for the isotopic cryogenic distillation of argon. We also present the successful results of isotopic cryogenic distillation of nitrogen with a prototype plant, operating the column at total reflux.

Potassium-40 ($^{40}$K) is a long-lived, naturally occurring radioactive isotope. The decay products are prominent backgrounds for many rare event searches, including those involving NaI-based scintillators. $^{40}$K also plays a role in geochronological dating techniques. The branching ratio of the electron capture directly to the ground state of argon-40 has never been measured, which can cause difficulty in interpreting certain results or can lead to lack of precision depending on the field and analysis technique. The KDK (Potassium (K) Decay (DK)) collaboration is measuring this decay. A composite method has a silicon drift detector with an enriched, thermally deposited $^{40}$K source inside the Modular Total Absorption Spectrometer. This setup has been characterized in terms of energy calibration, gamma tagging efficiency, live time and false negatives and positives. A complementary, homogeneous, method is also discussed; it employs a KSr$_2$I$_5$:Eu scintillator as source and detector., Comment: 20 pages, 24 figures, Submitted to NIM A

In this paper we investigate how to effectively deploy deep learning in practical industrial settings, such as robotic grasping applications. When a deep-learning based solution is proposed, usually lacks of any simple method to generate the training data. In the industrial field, where automation is the main goal, not bridging this gap is one of the main reasons why deep learning is not as widespread as it is in the academic world. For this reason, in this work we developed a system composed by a 3-DoF Pose Estimator based on Convolutional Neural Networks (CNNs) and an effective procedure to gather massive amounts of training images in the field with minimal human intervention. By automating the labeling stage, we also obtain very robust systems suitable for production-level usage. An open source implementation of our solution is provided, alongside with the dataset used for the experimental evaluation.

The general position problem in graph theory asks for the largest set $S$ of vertices of a graph $G$ such that no shortest path of $G$ contains more than two vertices of $S$. In this paper we consider a variant of the general position problem called the \emph{monophonic position problem}, obtained by replacing `shortest path' by `induced path'. We prove some basic properties and bounds for the monophonic position number of a graph and determine the monophonic position number of some graph families, including unicyclic graphs, complements of bipartite graphs and split graphs. We show that the monophonic position number of triangle-free graphs is bounded above by the independence number. We present realisation results for the general position number, monophonic position number and monophonic hull number. Finally we discuss the complexity of the monophonic position problem., Comment: Revised version. Arguments simplified and improved. Some corrections, including the result on split graphs

We report on a search for nuclear recoil signals from solar $^8$B neutrinos elastically scattering off xenon nuclei in XENON1T data, lowering the energy threshold from 2.6 keV to 1.6 keV. We develop a variety of novel techniques to limit the resulting increase in backgrounds near the threshold. No significant $^8$B neutrino-like excess is found in an exposure of 0.6 t $\times$ y. For the first time, we use the non-detection of solar neutrinos to constrain the light yield from 1-2 keV nuclear recoils in liquid xenon, as well as non-standard neutrino-quark interactions. Finally, we improve upon world-leading constraints on dark matter-nucleus interactions for dark matter masses between 3 GeV/c$^2$ and 11 GeV/c$^2$ by as much as an order of magnitude.

This work introduces a new framework, named SAFFIRE, to automatically extract a dominant recurrent image pattern from a set of image samples. Such a pattern shall be used to eliminate pose variations between samples, which is a common requirement in many computer vision and machine learning tasks. The framework is specialized here in the context of a machine vision system for automated product inspection. Here, it is customary to ask the user for the identification of an anchor pattern, to be used by the automated system to normalize data before further processing. Yet, this is a very sensitive operation which is intrinsically subjective and requires high expertise. Hereto, SAFFIRE provides a unique and disruptive framework for unsupervised identification of an optimal anchor pattern in a way which is fully transparent to the user. SAFFIRE is thoroughly validated on several realistic case studies for a machine vision inspection pipeline., Comment: 14 pages, 23 figures, 2 tables

We study simple nonequilibrium distributions describing a classical gas of particles interacting via a pair potential ${\phi}(x/{\epsilon})$, in the Boltzmann-Grad scaling ${\epsilon} \rightarrow 0$. We establish bounds for truncated correlations (cumulants) of arbitrary order as a function of the internal separation of particles in a cluster, showing exponential decay for finite range interactions., Comment: This note has been significantly changed over the past years by S. Simonella and R. Winter. Unfortunately, they were unable to contact A. di Stefano during this period. Since the current version is almost entirely different from the preliminary work with A. di Stefano, it could not be verified that A. di Stefano is in agreement with publishing the article with their name

We report the results of a search for the inelastic scattering of weakly interacting massive particles (WIMPs) in the XENON1T dark matter experiment. Scattering off $^{129}$Xe is the most sensitive probe of inelastic WIMP interactions, with a signature of a 39.6 keV de-excitation photon detected simultaneously with the nuclear recoil. Using an exposure of 0.89 tonne-years, we find no evidence of inelastic WIMP scattering with a significance of more than 2$\sigma$. A profile-likelihood ratio analysis is used to set upper limits on the cross-section of WIMP-nucleus interactions. We exclude new parameter space for WIMPs heavier than 100 GeV/c${}^2$, with the strongest upper limit of $3.3 \times 10^{-39}$ cm${}^2$ for 130 GeV/c${}^2$ WIMPs at 90\% confidence level., Comment: 9 pages, 4 figures

Future liquid-argon DarkSide-20k and ARGO detectors, designed for direct dark matter search, will be sensitive also to core-collapse supernova neutrinos, via coherent elastic neutrino-nucleus scattering. This interaction channel is flavor-insensitive with a high-cross section, enabling for a high-statistics neutrino detection with target masses of $\sim$50~t and $\sim$360~t for DarkSide-20k and ARGO, respectively. Thanks to the low-energy threshold of $\sim$0.5~keV$_{nr}$ achievable by exploiting the ionization channel, DarkSide-20k and ARGO have the potential to discover supernova bursts throughout our galaxy and up to the Small Magellanic Cloud, respectively, assuming a 11-M$_{\odot}$ progenitor star. We report also on the sensitivity to the neutronization burst, whose electron neutrino flux is suppressed by oscillations when detected via charged current and elastic scattering. Finally, the accuracies in the reconstruction of the average and total neutrino energy in the different phases of the supernova burst, as well as its time profile, are also discussed, taking into account the expected background and the detector response., Comment: 21 pages, 8 figures

Defining and reliably finding a canonical orientation for 3D surfaces is key to many Computer Vision and Robotics applications. This task is commonly addressed by handcrafted algorithms exploiting geometric cues deemed as distinctive and robust by the designer. Yet, one might conjecture that humans learn the notion of the inherent orientation of 3D objects from experience and that machines may do so alike. In this work, we show the feasibility of learning a robust canonical orientation for surfaces represented as point clouds. Based on the observation that the quintessential property of a canonical orientation is equivariance to 3D rotations, we propose to employ Spherical CNNs, a recently introduced machinery that can learn equivariant representations defined on the Special Orthogonal group SO(3). Specifically, spherical correlations compute feature maps whose elements define 3D rotations. Our method learns such feature maps from raw data by a self-supervised training procedure and robustly selects a rotation to transform the input point cloud into a learned canonical orientation. Thereby, we realize the first end-to-end learning approach to define and extract the canonical orientation of 3D shapes, which we aptly dub Compass. Experiments on several public datasets prove its effectiveness at orienting local surface patches as well as whole objects., Comment: Accepted to NeurIPS 2020

The quantum Rabi model is a widespread description of the coupling between a two-level system and a quantized single mode of an electromagnetic resonator. Issues about this model's gauge invariance have been raised. These issues become evident when the light-matter interaction reaches the so-called ultrastrong coupling regime. Recently, a modified quantum Rabi model able to provide gauge-invariant physical results (e.g., energy levels, expectation values of observables, quantum probabilities) in any interaction regime was introduced [Nature Physics 15, 803 (2019)]. Here we provide an alternative derivation of this result, based on the implementation in two-state systems of the gauge principle, which is the principle from which all the fundamental interactions in quantum field theory are derived. The adopted procedure can be regarded as the two-site version of the general method used to implement the gauge principle in lattice gauge theories. Applying this method, we also obtain the gauge-invariant quantum Rabi model for asymmetric two-state systems, and the multi-mode gauge-invariant quantum Rabi model beyond the dipole approximation., Comment: arXiv admin note: substantial text overlap with arXiv:2006.06583

Given a set R of robots, each one located at different vertices of an infinite regular tessellation graph, we aim to explore the Arbitrary Pattern Formation (APF) problem. Given a multiset F of grid vertices such that |R|=|F|, APF asks for a distributed algorithm that moves robots so as to reach a configuration similar to F. Similarity means that robots must be disposed as F regardless of translations, rotations, reflections. So far, as possible graph discretizing the Euclidean plane only the standard square grid has been considered in the context of the classical Look-Compute-Move model. However, it is natural to consider also the other regular tessellation graphs, that are triangular and hexagonal grids. We provide a resolution algorithm for APF when the initial configuration is asymmetric and the considered topology is any regular tessellation graph.