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Let $\mathcal{G}$ be the set of all the planar embeddings of a (not necessarily connected) $n$-vertex graph $G$. We present a bijection $\Phi$ from $\mathcal{G}$ to the natural numbers in the interval $[0 \dots |\mathcal{G}| - 1]$. Given a planar embedding $\mathcal{E}$ of $G$, we show that $\Phi(\mathcal{E})$ can be decomposed into a sequence of $O(n)$ natural numbers each describing a specific feature of $\mathcal{E}$. The function $\Phi$, which is a ranking function for $\mathcal{G}$, can be computed in $O(n)$ time, while its inverse unranking function $\Phi^{-1}$ can be computed in $O(n \alpha(n))$ time. The results of this paper can be of practical use to uniformly at random generating the planar embeddings of a graph $G$ or to enumerating such embeddings with amortized constant delay. Also, they can be used to counting, enumerating or uniformly at random generating constrained planar embeddings of $G$.

This work describes the elaboration of rutile titanium dioxide films with high refractive indices and low scattering by sol-gel process and controlled crystallization. The evolutions of the optical properties and crystalline structure of sol-gel processed titania coatings on fused silica were investigated for different thermal budgets of the annealing post-treatment using ellipsometry, spectrophotometry, X-ray diffraction and electronic microscopy. It reveals that anatase and rutile coatings with refractive indices of 2.5 and 2.7 can be prepared with associated optical loss of 0.5% and 1%, respectively, which are excellent compromise for applications in integrated photonics. These evolutions are associated to the thermally induced mass transfer and phase transitions occurring during thermal annealing that involves first the nucleation growth and sintering of anatase polyoriented nanocrystals, followed by the transformation into rutile polyoriented nanocrystals. Concomitantly, rutile crystals with (110) faces parallel to the surface consume surrounding anatase and rutile nanocrystals by diffusive sintering to yield micron-size rutile monocrystalline and monooriented platelets patchwork, exhibiting refractive index of 2.73 and 1.2% optical loss. The formation of these platelets is governed by surface energies and is responsible for the increase in optical loss.

Analog-on-Top Mixed Signal (AMS) Integrated Circuit (IC) design is a time-consuming process predominantly carried out by hand. Within this flow, usually, some area is reserved by the top-level integrator for the placement of digital blocks. Specific features of the area, such as size and shape, have a relevant impact on the possibility of implementing the digital logic with the required functionality. We present a Machine Learning (ML)-based evaluation methodology for predicting the feasibility of digital implementation using a set of high-level features. This approach aims to avoid time-consuming Place-and-Route trials, enabling rapid feedback between Digital and Analog Back-End designers during top-level placement., Comment: Author's version

Twisted hexagonal boron nitride (thBN) exhibits emergent ferroelectricity due to the formation of moir\'e superlattices with alternating AB and BA domains. These domains possess electric dipoles, leading to a periodic electrostatic potential that can be imprinted onto other 2D materials placed in its proximity. Here we demonstrate the remote imprinting of moir\'e patterns from twisted hexagonal boron nitride (thBN) onto monolayer MoSe2 and investigate the resulting changes in the exciton properties. We confirm the imprinting of moir\'e patterns on monolayer MoSe2 via proximity using Kelvin probe force microscopy (KPFM) and hyperspectral photoluminescence (PL) mapping. By developing a technique to create large ferroelectric domain sizes ranging from 1 {\mu}m to 8.7 {\mu}m, we achieve unprecedented potential modulation of 387 +- 52 meV. We observe the formation of exciton polarons due to charge redistribution caused by the antiferroelectric moir\'e domains and investigate the optical property changes induced by the moir\'e pattern in monolayer MoSe2 by varying the moir\'e pattern size down to 110 nm. Our findings highlight the potential of twisted hBN as a platform for controlling the optical and electronic properties of 2D materials for optoelectronic and valleytronic applications.

We study upward pointset embeddings (UPSEs) of planar $st$-graphs. Let $G$ be a planar $st$-graph and let $S \subset \mathbb{R}^2$ be a pointset with $|S|= |V(G)|$. An UPSE of $G$ on $S$ is an upward planar straight-line drawing of $G$ that maps the vertices of $G$ to the points of $S$. We consider both the problem of testing the existence of an UPSE of $G$ on $S$ (UPSE Testing) and the problem of enumerating all UPSEs of $G$ on $S$. We prove that UPSE Testing is NP-complete even for $st$-graphs that consist of a set of directed $st$-paths sharing only $s$ and $t$. On the other hand, for $n$-vertex planar $st$-graphs whose maximum $st$-cutset has size $k$, we prove that it is possible to solve UPSE Testing in $O(n^{4k})$ time with $O(n^{3k})$ space, and to enumerate all UPSEs of $G$ on $S$ with $O(n)$ worst-case delay, using $O(k n^{4k} \log n)$ space, after $O(k n^{4k} \log n)$ set-up time. Moreover, for an $n$-vertex $st$-graph whose underlying graph is a cycle, we provide a necessary and sufficient condition for the existence of an UPSE on a given poinset, which can be tested in $O(n \log n)$ time. Related to this result, we give an algorithm that, for a set $S$ of $n$ points, enumerates all the non-crossing monotone Hamiltonian cycles on $S$ with $O(n)$ worst-case delay, using $O(n^2)$ space, after $O(n^2)$ set-up time., Comment: This is the long version of a paper to appear at the 32nd International Symposium on Graph Drawing and Network Visualization (GD '24)

In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on machine learning, where model selection can introduce a bias towards specific families of new physics signals. We show that it is beneficial to combine different tests, characterised by distinct choices of hyperparameters, and that performances comparable to the best available test are generally achieved while providing a more uniform response to various types of anomalies. Focusing on the New Physics Learning Machine, a methodology to perform a signal-agnostic likelihood-ratio test, we explore a number of approaches to multiple testing, such as combining p-values and aggregating test statistics., Comment: 17 pages, 5 tables, 6 figures

We present a method to prepare non-classical states of the electromagnetic field in a microwave resonator. It is based on a controlled gate that applies a squeezing operation on a SQUID-terminated resonator conditioned on the state of a dispersively coupled qubit. This controlled-squeeze gate, when combined with Gaussian operations on the resonator, is universal. We explore the use of this tool to map an arbitrary qubit state into a superposition of squeezed states. In particular, we target a bosonic code with well-defined superparity which makes photon losses detectable by nondemolition parity measurements. We analyze the possibility of implementing this using state-of-the-art circuit QED tools and conclude that it is within reach of current technologies., Comment: 5 pages, 2 figures and supplemental material

Biomolecular condensates form by phase separation of biological polymers. The cellular functions of the resulting membraneless organelles are closely linked to their physical properties over a wide range of length- and timescales: From the nanosecond dynamics of individual molecules and their interactions, to the microsecond translational diffusion of molecules in the condensates, to their viscoelastic properties at the mesoscopic scale. However, it has remained unclear how to quantitatively link these properties across scales. Here we address this question by combining single-molecule fluorescence, correlation spectroscopy, microrheology, and large-scale molecular dynamics simulations on different condensates that are formed by complex coacervation and span about two orders of magnitude in viscosity and their dynamics at the molecular scale. Remarkably, we find that the absolute timescale of protein chain dynamics in the dense phases can be quantitatively and accurately related to translational diffusion and condensate viscosities by Rouse theory of polymer solutions including entanglement. The simulations indicate that the observed wide range of dynamics arises from different contact lifetimes between amino acid residues, which in the mean-field description of the polymer model cause differences in the friction acting on the chains. These results suggest that remarkably simple physical principles can relate the mesoscale properties of biomolecular condensates to their dynamics at the nanoscale.

In this work, we studied photon generation due to the Dynamical Casimir Effect (DCE) in a one dimensional (1+1) double superconducting cavity. The cavity consists of two perfectly conducting mirrors and a dielectric membrane of infinitesimal depth that effectively couples two cavities. The total length of the double cavity $L$, the difference in length between the two cavities $\Delta L$, and the electric susceptibility $\chi$ and conductivity $v$ of the dielectric membrane are tunable parameters. All four parameters are treated as independent and are allowed to be tuned at the same time, even with different frequencies. We analyzed the cavity's energy spectra under different conditions, finding a transition between two distinct regimes that is accurately described by $k_c=\sqrt{v/\chi}$. In particular, a lowest energy mode is forbidden in one of the regimes while it is allowed in the other. We compared analytical approximations obtained through the Multiple Scale Analysis method with exact numeric solutions, obtaining the typical results when $\chi$ is not being tuned. However, when the susceptibility $\chi$ is tuned, different behaviours (such as oscillations in the number of photons of a cavity prepared in a vacuum state) might arise if the frequencies and amplitudes of all parameters are adequate. These oscillations can be considered as adiabatic shortcuts where all generated photons are eventually destroyed. Finally, we present an equivalent quantum circuit that would allow to experimentally simulate the DCE under the studied conditions., Comment: 22 pages, 20 figures

Signal-agnostic data exploration based on machine learning could unveil very subtle statistical deviations of collider data from the expected Standard Model of particle physics. The beneficial impact of a large training sample on machine learning solutions motivates the exploration of increasingly large and inclusive samples of acquired data with resource efficient computational methods. In this work we consider the New Physics Learning Machine (NPLM), a multivariate goodness-of-fit test built on the Neyman-Pearson maximum-likelihood-ratio construction, and we address the problem of testing large size samples under computational and storage resource constraints. We propose to perform parallel NPLM routines over batches of the data, and to combine them by locally aggregating over the data-to-reference density ratios learnt by each batch. The resulting data hypothesis defining the likelihood-ratio test is thus shared over the batches, and complies with the assumption that the expected rate of new physical processes is time invariant. We show that this method outperforms the simple sum of the independent tests run over the batches, and can recover, or even surpass, the sensitivity of the single test run over the full data. Beside the significant advantage for the offline application of NPLM to large size samples, the proposed approach offers new prospects toward the use of NPLM to construct anomaly-aware summary statistics in quasi-online data streaming scenarios.

[Abridged] ESO 141-G55 is a nearby X-ray bright BLS1, which has been classified as a bare AGN due to the lack of warm absorption along its line-of-sight, providing an unhampered view into its disc-corona system. We aim to probe its disc-corona system thanks to the first simultaneous XMM-Newton and NuSTAR observation obtained on October 1-2, 2022. We carry out the X-ray broadband spectral analysis to determine the dominant process(es) at work, as well as the SED analysis to determine the disc-corona properties. The simultaneous broadband X-ray spectrum of ESO 141-G55 is characterised by the presence of a prominent smooth soft X-ray excess, a broad Fe K emission line and a significant Compton hump. The RGS spectra confirmed the lack of intrinsic warm-absorbing gas along our line of sight in the AGN rest frame, confirming that it is still in a bare state. However, soft X-ray emission lines are observed indicating substantial warm gas out of our line of sight. The intermediate inclination of the disc-corona system, ~43{\deg}, may offer us a favourable configuration to observe UFOs from the disc, but none is found in this 2022 observation, contrary to a previous 2007 XMM-Newton one. Relativistic reflection alone on a standard disc is ruled out from the X-ray broadband analysis, while a combination of soft and hard Comptonisation by a warm and hot corona (relagn), plus relativistic reflection (reflkerrd) reproduces its SED quite well. The hot corona temperature is very hot, ~140 keV, much higher than about 80% of the AGNs, whereas the warm corona temperature, ~0.3 keV, is similar to the values found in other sub-Eddington AGNs. ESO 141-G55 is accreting at a moderate Eddington accretion rate (~10--20%). Our analysis points to a significant contribution of an optically-thick warm corona to both the soft X-ray and UV emission in ESO 141-G55., Comment: 16 pages, 14 figures, 3 appendices; accepted for publication in Astronomy & Astrophysics

This article demonstrates the effectiveness of employing a deep learning model in an optimization pipeline. Specifically, in a generic exact algorithm for a NP problem, multiple heuristic criteria are usually used to guide the search of the optimum within the set of all feasible solutions. In this context, neural networks can be leveraged to rapidly acquire valuable information, enabling the identification of a more expedient path in this vast space. So, after the explanation of the tackled traveling salesman problem, the implemented branch and bound for its classical resolution is described. This algorithm is then compared with its hybrid version termed "graph convolutional branch and bound" that integrates the previous branch and bound with a graph convolutional neural network. The empirical results obtained highlight the efficacy of this approach, leading to conclusive findings and suggesting potential directions for future research., Comment: Submitted to European Journal of Operational Research

V2X technologies will become widespread in the next generation of passenger cars, and enable the development of novel vehicle control functionalities. Although a wide literature describes the energy efficiency benefits of V2X connectivity, e.g., in terms of vehicle speed profiling and platooning, there is a gap in the analysis of the potential of vehicle connectivity in enhancing the performance of active safety control systems. To highlight the impact vehicle connectivity could have on future active safety systems, this paper presents two novel control functions for connected vehicles, benefitting from the precise knowledge of the expected path and tire-road friction conditions ahead, as well as the current position of the ego vehicle. These functions, developed within recent and ongoing European projects, are: i) pre-emptive traction control; and ii) pre-emptive braking control., Comment: 8 pages, 6 figures, Published in the Transport Research Arena (TRA) Conference, Lisbon, Portugal, 2022

Running deep neural networks for large medical images is a resource-hungry and time-consuming task with centralized computing. Outsourcing such medical image processing tasks to hybrid clouds has benefits, such as a significant reduction of execution time and monetary cost. However, due to privacy concerns, it is still challenging to process sensitive medical images over clouds, which would hinder their deployment in many real-world applications. To overcome this, we first formulate the overall optimization objectives of the privacy-preserving distributed system model, i.e., minimizing the amount of information about the private data learned by the adversaries throughout the process, reducing the maximum execution time and cost under the user budget constraint. We propose a novel privacy-preserving and cost-effective method called PriCE to solve this multi-objective optimization problem. We performed extensive simulation experiments for artifact detection tasks on medical images using an ensemble of five deep convolutional neural network inferences as the workflow task. Experimental results show that PriCE successfully splits a wide range of input gigapixel medical images with graph-coloring-based strategies, yielding desired output utility and lowering the privacy risk, makespan, and monetary cost under user's budget., Comment: Acccepted at Europar 2024

In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manyfold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19 detection, and the clinical validation of the developed solution by expert radiologists. The proposed detection model is based on a two-step approach that, paired with state-of-the-art debiasing, provides reliable results. Most importantly, our investigation includes the actual usage of the diagnosis aid tool by radiologists, allowing us to assess the real benefits in terms of accuracy and time efficiency. Project homepage: https://corsa.di.unito.it/, Comment: Accepted at 21st IEEE International Symposium on Biomedical Imaging (ISBI)

Motivated by the quantum description of gauge theories, we study the cosmological effects of relaxing the Hamiltonian and momentum constraints in general relativity and Gauss' law in electromagnetism. We show that the unconstrained theories have new source terms that mimic a pressureless dust and a charge density that only follows geodesics. The source terms may be the simplest explanation for dark matter and generically predict a charged component. We comment that discovery of such terms would rule out inflation and be a direct probe of the initial conditions of the universe., Comment: 6 pages

Fermion soliton stars are a consistent model of exotic compact objects which involve a nonlinear interaction between a real scalar field and fermions through a Yukawa term. This interaction results in an effective fermion mass that depends upon the vacuum structure in the scalar potential. In this work we investigate the tidal deformations of fermion soliton stars and compute the corresponding tidal Love numbers for different model parameters. Furthermore, we discuss the existence of approximate universal relations for the electric and magnetic tidal deformabilities of these stars, and compare them with other solutions of general relativity, such as neutron stars or boson stars. These relations for fermion soliton stars are less universal than for neutron stars, but they are sufficiently different from the ordinary neutron star case that a measurement of the electric and magnetic tidal Love numbers (as potentially achievable by next-generation gravitational wave detectors) can be used to disentangle these families of compact objects. Finally, we discuss the conditions for tidal disruption of fermion soliton stars in a binary system and estimate the detectability of the electromagnetic signal associated with such tidal disruption events., Comment: 15 pages, 4 figures. v2: new figure added, matches version accepted in PRD

MISTRAL is the new Faint Object Spectroscopic Camera mounted at the folded Cassegrain focus of the 1.93m telescope of Haute-Provence Observatory. We describe the design and components of the instrument and give some details about its operation. We emphasise in particular the various observing modes and the performances of the detector. A short description is also given about the working environment. Various types of objects, including stars, nebulae, comets, novae, galaxies have been observed during various test phases to evaluate the performances of the instrument. The instrument covers the range of 4000 to 8000A with the blue setting, or from 6000 to 10000A with the red setting, at an average spectral resolution of 700. Its peak efficiency is about 22% at 6000A. In spectroscopy, a limiting magnitude of 19.5 can be achieved for a point source in one hour with a signal to noise of 3 in the continuum (and better if emission lines are present). In imaging mode, limiting magnitudes of 20-21 can be obtained in 10-20mn (with average seing conditions of 2.5 arcsec at OHP). The instrument is very users-friendly and can be put into operations in less than 15mn (rapid change-over from the other instrument in use) if required by the science (like for Gamma-Rays Bursts). Some first scientific results are described for various types of objects, and in particular for the follow-up of GRBs. While some further improvements are still under way, in particular to ease the switch from blue to red setting and add more grisms or filters, MISTRAL is ready for the follow-up of transients and other variable objects, in the soon-to-come era of e.g. the SVOM satellite and of the Rubin telescope., Comment: Accepted in A&A

In 2019, more than 5 million providers cared for one or more children either in their own home or in a child's home. Home-based child care (HBCC) providers are a varied group that includes listed providers and unlisted providers who do and do not receive payment. HBCC is especially prevalent in communities of color, communities with high concentrations of people from immigrant backgrounds, areas of concentrated poverty, and rural communities. Yet, research on HBCC lags behind research on center-based child care and early education (CCEE), and the least is known about unlisted providers who do not appear on state or national provider lists and work outside the formal systems supporting CCEE programs. This brief focuses on describing the characteristics of the communities in which unlisted HBCC providers live and, for many, where they typically care for children. It uses the 2019 National Survey of Early Care and Education (NSECE), the American Community Survey (ACS), and the Child Opportunity Index (COI) 2.0.

Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on an temporally-independent cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation cohort and 0.77 in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n=540), among patients with AI-CAC=0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC>0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank=0.013). Conclusion. The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest x-ray with elevated sensitivity, and to predict ASCVD events with elevated negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation., Comment: Submitted to European Heart Journal - Cardiovascular Imaging Added also the additional material 44 pages (30 main paper, 14 additional material), 14 figures (5 main manuscript, 9 additional material)

Single-photon emitters serve as building blocks for many emerging concepts in quantum photonics. The recent identification of bright, tunable, and stable emitters in hexagonal boron nitride (hBN) has opened the door to quantum platforms operating across the infrared to ultraviolet spectrum. While it is widely acknowledged that defects are responsible for single-photon emitters in hBN, crucial details regarding their origin, electronic levels, and orbital involvement remain unknown. Here, we employ a combination of resonant inelastic X-ray scattering and photoluminescence spectroscopy in defective hBN unveiling an elementary excitation at 285 meV that gives rise to a plethora of harmonics correlated with single-photon emitters. We discuss the importance of N $\pi^*$ antibonding orbitals in shaping the electronic states of the emitters. The discovery of the elementary excitations of hBN provides new fundamental insights into quantum emission in low-dimensional materials, paving the way for future investigations in other platforms.

We consider the problem of scheduling non preemptively a set of jobs on parallel identical machines with prior setup operations on a single shared server, where the objective is to minimise the makespan. We develop an arc-flow formulation to the problem with two multigraphs, one for the machines and one for the server, with a same set of nodes representing points in time, and arcs associated with job execution, and with machines or server idleness. The resulting Flow-Flow formulation (FFF) and its tuned version (FFT) are compared with the best existing model in the literature, TIV I, on benchmark instances with up to 200 jobs and 7 machines. Computational results showed that our Flow-Flow models outperformed TIV I for instances with 100 jobs and produced optimal solutions to the vast majority of problems with 150 and 200 jobs for which TIV I failed to even find integer solutions within the standard limited runtime of 3600 s. When non optimal, solutions provided by our models exhibited very low gaps to best bound.

The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score.

Efficiently processing medical images, such as whole slide images in digital pathology, is essential for timely diagnosing high-risk diseases. However, this demands advanced computing infrastructure, e.g., GPU servers for deep learning inferencing, and local processing is time-consuming and costly. Besides, privacy concerns further complicate the employment of remote cloud infrastructures. While previous research has explored privacy and security-aware workflow scheduling in hybrid clouds for distributed processing, privacy-preserving data splitting, optimizing the service allocation of outsourcing computation on split data to the cloud, and privacy evaluation for large medical images still need to be addressed. This study focuses on tailoring a virtual infrastructure within a hybrid cloud environment and scheduling the image processing services while preserving privacy. We aim to minimize the use of untrusted nodes, lower monetary costs, and reduce execution time under privacy, budget, and deadline requirements. We consider a two-phase solution and develop 1) a privacy-preserving data splitting algorithm and 2) a greedy Pareto front-based algorithm for optimizing the service allocation. We conducted experiments with real and simulated data to validate and compare our method with a baseline. The results show that our privacy mechanism design outperforms the baseline regarding the average lower band on individual privacy and information gain for privacy evaluation. In addition, our approach can obtain various Pareto optimal-based allocations with users' preferences on the maximum number of untrusted nodes, budget, and time threshold. Our solutions often dominate the baseline's solution and are superior on a tight budget. Specifically, our approach has been ahead of baseline, up to 85.2% and 6.8% in terms of the total financial and time costs, respectively.

This paper examines the Flexible Algorithm (FlexAlgo) for its potential to enable user-driven path control in intra-domain Segment Routing (SR) enabled networks. FlexAlgo is a relatively new approach to intra-domain routing that allows multiple custom algorithms to coexist within a single domain. This capability has the potential to provide users with greater control over the paths their data takes through a network. The research includes a thorough investigation of the FlexAlgo approach, including an examination of its underlying techniques, as well as a practical implementation of a FlexAlgo-based solution. We depict performed experiments where we implemented FlexAlgo in three different scenarios. We also present how we developed an automated tool for users to control traffic steering using preferred metrics and constraints. The results of this investigation demonstrate the capabilities of FlexAlgo as a means of enabling user-driven path control and therefore increase security and trust of users towards the network.

We are developing the Cryogenic AntiCoincidence detector (CryoAC) of the ATHENA X-IFU spectrometer. It is a TES-based particle detector aimed to reduce the background of the instrument. Here, we present the result obtained with the last CryoAC single-pixel prototype. It is based on a 1 cm2 silicon absorber sensed by a single 2mm x 1mm Ir/Au TES, featuring an on-chip heater for calibration and diagnostic purposes. We have illuminated the sample with 55Fe (6 keV line) and 241Am (60 keV line) radioactive sources, thus studying the detector response and the heater calibration accuracy at low energy. Furthermore, we have operated the sample in combination with a past-generation CryoAC prototype. Here, by analyzing the coincident detections between the two detectors, we have been able to characterize the background spectrum of the laboratory environment and disentangle the primary (i.e. cosmic muons) and secondaries (mostly secondary photons and electrons) signatures in the spectral shape., Comment: Accepted for publication in the Journal of Low Temperature Physics for LTD-20 special issue

We consider a theory in which a real scalar field is Yukawa-coupled to a fermion and has a potential with two non-degenerate vacua. If the coupling is sufficiently strong, a collection of N fermions deforms the true vacuum state, creating energetically-favored false-vacuum pockets in which fermions are trapped. We embed this model within General Relativity and prove that it admits self-gravitating compact objects where the scalar field acquires a non-trivial profile due to non-perturbative effects. We discuss some applications of this general mechanism: i) neutron soliton stars in low-energy effective QCD, which naturally happen to have masses around 2 solar masses and radii around 10 km even without neutron interactions; ii) Higgs false-vacuum pockets in and beyond the Standard Model; iii) dark soliton stars in models with a dark sector. In the latter two examples, we find compelling solutions naturally describing centimeter-size compact objects with masses around 10^-6 solar masses, intriguingly in a range compatible with the OGLE+HSC microlensing anomaly. Besides these interesting examples, the mechanism of non-perturbative vacuum scalarization may play a role in various contexts in and beyond the Standard Model, providing a support mechanism for new compact objects that can form in the early universe, can collapse into primordial black holes through accretion past their maximum mass, and serve as dark matter candidates., Comment: 12 pages, 6 figures. v2: matches version accepted in PRD