Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent developments in DL accelerators, focusing on their role in meeting the performance demands of HPC applications. We explore cutting-edge approaches to DL acceleration, covering not only GPU- and TPU-based platforms but also specialized hardware such as FPGA- and ASIC-based accelerators, Neural Processing Units, open hardware RISC-V-based accelerators, and co-processors. This survey also describes accelerators leveraging emerging memory technologies and computing paradigms, including 3D-stacked Processor-In-Memory, non-volatile memories like Resistive RAM and Phase Change Memories used for in-memory computing, as well as Neuromorphic Processing Units, and Multi-Chip Module-based accelerators. Furthermore, we provide insights into emerging quantum-based accelerators and photonics. Finally, this survey categorizes the most influential architectures and technologies from recent years, offering readers a comprehensive perspective on the rapidly evolving field of deep learning acceleration.

Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performance Computing (HPC) and edge platforms, leading to a wide variety of proposals for specialized deep learning architectures and hardware accelerators. The design of such architectures and accelerators requires a multidisciplinary approach combining expertise from several areas, from machine learning to computer architecture, low-level hardware design, and approximate computing. Several methodologies and tools have been proposed to improve the process of designing accelerators for deep learning, aimed at maximizing parallelism and minimizing data movement to achieve high performance and energy efficiency. This paper critically reviews influential tools and design methodologies for Deep Learning accelerators, offering a wide perspective in this rapidly evolving field. This work complements surveys on architectures and accelerators by covering hardware-software co-design, automated synthesis, domain-specific compilers, design space exploration, modeling, and simulation, providing insights into technical challenges and open research directions.

The cosmic microwave background (CMB) experiments have reached an era of unprecedented precision and complexity. Aiming to detect the primordial B-mode polarization signal, these experiments will soon be equipped with $10^{4}$ to $10^{5}$ detectors. Consequently, future CMB missions will face the substantial challenge of efficiently processing vast amounts of raw data to produce the initial scientific outputs - the sky maps - within a reasonable time frame and with available computational resources. To address this, we introduce BrahMap, a new map-making framework that will be scalable across both CPU and GPU platforms. Implemented in C++ with a user-friendly Python interface for handling sparse linear systems, BrahMap employs advanced numerical analysis and high-performance computing techniques to maximize the use of super-computing infrastructure. This work features an overview of the BrahMap's capabilities and preliminary performance scaling results, with application to a generic CMB polarization experiment.

A simple effective model for the intermediate-density regime is constructed from the high-density effective theory of quantum chromodynamics (QCD). In the effective model, under a renormalization-group (RG) scaling towards low momenta, the original QCD interactions lead to four-quark contact interactions for the relevant quark and hole modes around the Fermi surface. The contact interaction in the scalar channel can be traced back to zero-sound-type collinear quark scattering near the Fermi surface in an instanton background. The quark and hole states in opposite directions of a given Fermi velocity form the collective scalar bosonic mode $\sigma$. The magnitude of $\sigma$ is investigated via the non-perturbative Functional Renormalization Group (FRG) evolution of the effective average action from the ultraviolet (UV) to the infrared (IR). In the mean background-field approximation for $\sigma$, nontrivial minima ($\bar{\sigma} \neq 0$) are found in the IR limit of the effective average action. A nonvanishing $\bar{\sigma}$ corresponds to condensation of quark and hole states in opposite directions of a given Fermi velocity, in a thin shell-like structure in momentum space around the Fermi surface. This looks similar to the shell-like baryon distribution in momentum space assumed in the quarkyonic-matter concept. However, when including a dynamic bosonic $\sigma$-mode in the RG flow, we find that its diffusive nature destroys the quark-hole condensate, i.e., the IR potential does not show any minima beyond the trivial one.

In the recent years, argon-based experiments looking for Dark Matter in the Universe have explored the non-standard scenario in which Dark Matter is made by low-mass Weakly Interacting Massive Particles, of mass in the range of 1-10 GeV instead of the canonical hundreds of GeV. Detecting such particles is challenging, as their expected signatures are nuclear recoils with energies below 10 keV, observable solely via ionization. This necessitates a precise understanding of the detector response in this energy regime, which remains incomplete for argon. To address this, the ReD experiment was developed within the framework of the DarkSide-20k Collaboration to produce and characterize few-keV nuclear recoils. A compact dual-phase argon Time Projection Chamber (TPC) was irradiated with neutrons from a Cf252 source, to produce Ar recoils in the energy range of interest via (n,n') elastic scattering. A downstream spectrometer composed of 18 plastic scintillators detected the neutrons scattered off Ar nuclei, enabling recoil energy reconstruction via two-body kinematics. The ionization yield Qy of argon, defined as the number of electrons produced per unit energy deposit, was measured in a model-independent way between 2 and 10 keV. These measurements extend direct experimental coverage well below the previous limit of approximately 7 keV. The results are consistent with existing data above 7 keV, while they indicate a higher Qy at lower energies.

We present ForSE+, a Python package that produces non-Gaussian diffuse Galactic thermal dust emission maps at arcminute angular scales and that has the capacity to generate random realizations of small scales. This represents an extension of the ForSE (Foreground Scale Extender) package, which was recently proposed to simulate non-Gaussian small scales of thermal dust emission using generative adversarial networks (GANs). With the input of the large-scale polarization maps from observations, ForSE+ has been trained to produce realistic polarized small scales at 3' following the statistical properties, mainly the non-Gaussianity, of observed intensity small scales, which are evaluated through Minkowski functionals. Furthermore, by adding different realizations of random components to the large-scale foregrounds, we show that ForSE+ is able to generate small scales in a stochastic way. In both cases, the output small scales have a similar level of non-Gaussianity compared with real observations and correct amplitude scaling as a power law. These realistic new maps will be useful, in the future, to understand the impact of non-Gaussian foregrounds on the measurements of the cosmic microwave background (CMB) signal, particularly on the lensing reconstruction, de-lensing, and the detection of cosmological gravitational waves in CMB polarization B-modes.

Quantum computing leverages the principles of quantum mechanics to perform computations far beyond the capabilities of classical systems, particularly in fields such as cryptography and optimization. However, current quantum programming languages often require low-level implementation, posing significant barriers for many developers due to their steep learning curve and limited abstraction. In response, we introduce \textbf{Qutes}, a high-level quantum programming language designed to simplify quantum algorithm development while maintaining the flexibility required for advanced applications. By abstracting complex quantum operations and allowing intuitive expressions through high-level constructs, Qutes enables users to write efficient quantum programs without extensive knowledge of quantum mechanics or circuit design. Built upon Qiskit, Qutes translates its syntax directly into executable quantum code, facilitating seamless integration with quantum hardware. This paper provides an overview of the language's architecture, core functionalities, and its ability to unify classical and quantum operations within a single framework. Additionally, we demonstrate Qutes' application in key quantum algorithms, showcasing its potential to make quantum programming more accessible and practical for a wider range of developers and researchers.

The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is determined, based on a full simulation, reconstruction, and event selection of the far detector and a full simulation and parameterized analysis of the near detector. Detailed uncertainties due to the flux prediction, neutrino interaction model, and detector effects are included. DUNE will resolve the neutrino mass ordering to a precision of 5$\sigma$, for all $\delta_{\mathrm{CP}}$ values, after 2 years of running with the nominal detector design and beam configuration. It has the potential to observe charge-parity violation in the neutrino sector to a precision of 3$\sigma$ (5$\sigma$) after an exposure of 5 (10) years, for 50\% of all $\delta_{\mathrm{CP}}$ values. It will also make precise measurements of other parameters governing long-baseline neutrino oscillation, and after an exposure of 15 years will achieve a similar sensitivity to $\sin^{2} 2\theta_{13}$ to current reactor experiments.

In the present-day scenario, Large Language Models (LLMs) are establishing their presence as powerful instruments permeating various sectors of society. While their utility offers valuable support to individuals, there are multiple concerns over potential misuse. Consequently, some academic endeavors have sought to introduce watermarking techniques, characterized by the inclusion of markers within machine-generated text, to facilitate algorithmic identification. This research project is focused on the development of a novel methodology for the detection of synthetic text, with the overarching goal of ensuring the ethical application of LLMs in AI-driven text generation. The investigation commences with replicating findings from a previous baseline study, thereby underscoring its susceptibility to variations in the underlying generation model. Subsequently, we propose an innovative watermarking approach and subject it to rigorous evaluation, employing paraphrased generated text to asses its robustness. Experimental results highlight the robustness of our proposal compared to the~\cite{aarson} watermarking method.

Polarized foreground emission from the Galaxy is one of the biggest challenges facing current and upcoming cosmic microwave background (CMB) polarization experiments. We develop new models of polarized Galactic dust and synchrotron emission at CMB frequencies that draw on the latest observational constraints, that employ the ``polarization fraction tensor'' framework to couple intensity and polarization in a physically motivated way, and that allow for stochastic realizations of small-scale structure at sub-arcminute angular scales currently unconstrained by full-sky data. We implement these models into the publicly available Python Sky Model (PySM) software and additionally provide PySM interfaces to select models of dust and CO emission from the literature. We characterize the behavior of each model by quantitatively comparing it to observational constraints in both maps and power spectra, demonstrating an overall improvement over previous PySM models. Finally, we synthesize models of the various Galactic foreground components into a coherent suite of three plausible microwave skies that span a range of astrophysical complexity allowed by current data.

JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day, therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz in the default fiducial volume, above an energy threshold of 0.7 MeV.

(abridged) The far-UV wavelength range (912-2000A) provides access to atomic and molecular transitions of many species the interstellar medium (ISM), circumgalactic medium (CGM), and intergalactic medium, within phases spanning a wide range of ionization, density, temperature, and molecular gas fraction. Far-UV space telescopes have enabled detailed studies of the ISM in the Milky Way thanks to absorption features appearing in the UV spectra of hot stars and yielding fundamental insights into the composition and physical characteristics of all phases of the ISM along with the processes that influence them. However, we have yet to design a spectrometer able to observe the full UV domain at resolving power R>10^5 with a signal-to-noise ratio SNR>500. Such a resolution is necessary to resolve lines from both the cold molecular hydrogen and the warm metal ions with a turbulent velocity of about 1 km s-1, and to differentiate distinct velocity components. Future UV spectroscopic studies of the Milky Way ISM must revolutionize our understanding of the ISM as a dynamical, unstable, and magnetized medium, and rise to the challenge brought forward by current theories. Another interesting prospect is to transpose the same level of details that has been reached for the Milky Way to the ISM in external galaxies, in particular in metal-poor galaxies, where the ISM chemical composition, physical conditions, and topology change dramatically, with significant consequences on the star-formation properties. Finally, we need to be able to perform statistical analyses of background quasar lines of sight intersecting the CGM of galaxies at various redshifts and to comprehend the role of gas exchanges and flows for galaxy evolution.

With the aim of improving our knowledge about the nature of the progenitors of low-luminosity Type II plateau supernovae (LL SNe IIP), we made radiation-hydrodynamical models of the well-sampled LL SNe IIP 2003Z, 2008bk and 2009md. For these three SNe we infer explosion energies of $0.16$-$0.18$ foe, radii at explosion of $1.8$-$3.5 \times 10^{13}$ cm, and ejected masses of $10$-$11.3$\Msun. The estimated progenitor mass on the main sequence is in the range $\sim 13.2$-$15.1$\Msun\, for SN 2003Z and $\sim 11.4$-$12.9$\Msun\, for SNe 2008bk and 2009md, in agreement with estimates from observations of the progenitors. These results together with those for other LL SNe IIP modelled in the same way, enable us also to conduct a comparative study on this SN sub-group. The results suggest that: a) the progenitors of faint SNe IIP are slightly less massive and have less energetic explosions than those of intermediate-luminosity SNe IIP, b) both faint and intermediate-luminosity SNe IIP originate from low-energy explosions of red (or yellow) supergiant stars of low-to-intermediate mass, c) some faint objects may also be explained as electron-capture SNe from massive super-asymptotic giant branch stars, and d) LL SNe IIP form the underluminous tail of the SNe IIP family, where the main parameter "guiding" the distribution seems to be the ratio of the total explosion energy to the ejected mass. Further hydrodynamical studies should be performed and compared to a more extended sample of LL SNe IIP before drawing any conclusion on the relevance of fall-back to this class of events.

We present an all-sky map of the synchrotron spectral index and curvature between 45 and 2300 MHz at a resolution of 1 degree calculated from a combination of numerous partial sky empirical measurements. We employ a least-squares parametric fit which relies on removing a free-free emission template and a component separation technique which fits for both synchrotron and free-free emission. We compare our diffuse sky model estimates against those derived from the models widely used in the community (e.g. pysm3 and GSM) employing external datasets that were not included in the estimation process. Our evaluation focuses on identifying the enhanced consistency at both the map level and in pixel-to-pixel correlations, allowing for a more robust verification of our model's performance. We find our parametric, least-squares synchrotron estimate to be the most reliable across radio frequencies as it consistently provides sky models with average accuracies (when compared to empirical data) of around 20 per cent, whilst other model performances range on average between 10 and 70 per cent accurate. The results obtained have been made publicly accessible online and can be utilized to further develop and refine models of Galactic synchrotron emission.

We consider the problem of localizing visitors in a cultural site from egocentric (first person) images. Localization information can be useful both to assist the user during his visit (e.g., by suggesting where to go and what to see next) and to provide behavioral information to the manager of the cultural site (e.g., how much time has been spent by visitors at a given location? What has been liked most?). To tackle the problem, we collected a large dataset of egocentric videos using two cameras: a head-mounted HoloLens device and a chest-mounted GoPro. Each frame has been labeled according to the location of the visitor and to what he was looking at. The dataset is freely available in order to encourage research in this domain. The dataset is complemented with baseline experiments performed considering a state-of-the-art method for location-based temporal segmentation of egocentric videos. Experiments show that compelling results can be achieved to extract useful information for both the visitor and the site-manager.

We compare the Gaia DR2 and Gaia EDR3 performances in the study of the Magellanic Clouds and show the clear improvements in precision and accuracy in the new release. We also show that the systematics still present in the data make the determination of the 3D geometry of the LMC a difficult endeavour; this is at the very limit of the usefulness of the Gaia EDR3 astrometry, but it may become feasible with the use of additional external data. We derive radial and tangential velocity maps and global profiles for the LMC for the several subsamples we defined. To our knowledge, this is the first time that the two planar components of the ordered and random motions are derived for multiple stellar evolutionary phases in a galactic disc outside the Milky Way, showing the differences between younger and older phases. We also analyse the spatial structure and motions in the central region, the bar, and the disc, providing new insights into features and kinematics. Finally, we show that the Gaia EDR3 data allows clearly resolving the Magellanic Bridge, and we trace the density and velocity flow of the stars from the SMC towards the LMC not only globally, but also separately for young and evolved populations. This allows us to confirm an evolved population in the Bridge that is slightly shift from the younger population. Additionally, we were able to study the outskirts of both Magellanic Clouds, in which we detected some well-known features and indications of new ones.

Risk assessment for rare events is essential for understanding systemic stability in complex systems. As rare events are typically highly correlated, it is important to study heavy-tailed multivariate distributions of the relevant variables, i.e. their joint probability density functions. Only for few systems, such investigation have been performed. Statistical models are desirable that describe heavy-tailed multivariate distributions, in particular when non-stationarity is present as is typically the case in complex systems. Recently, we put forward such a model based on a separation of time scales. By utilizing random matrices, we showed that the fluctuations of the correlations lift the tails. Here, we present formulae and methods to carry out a data comparisons for complex systems. There are only few fit parameters. Compared to our previous results, we manage to remove in the algebraic cases one out of the two, respectively three, fit parameters which considerably facilitates applications. Furthermore, we explicitly work out the moments of our model distributions. In a forthcoming paper we will apply our model to financial markets.

We present the early installment of the third Gaia data release, Gaia EDR3, consisting of astrometry and photometry for 1.8 billion sources brighter than magnitude 21, complemented with the list of radial velocities from Gaia DR2. Gaia EDR3 contains celestial positions and the apparent brightness in G for approximately 1.8 billion sources. For 1.5 billion of those sources, parallaxes, proper motions, and the (G_BP-G_RP) colour are also available. The passbands for G, G_BP, and G_RP are provided as part of the release. For ease of use, the 7 million radial velocities from Gaia DR2 are included in this release, after the removal of a small number of spurious values. New radial velocities will appear as part of Gaia DR3. Finally, Gaia EDR3 represents an updated materialisation of the celestial reference frame (CRF) in the optical, the Gaia-CRF3, which is based solely on extragalactic sources. The creation of the source list for Gaia EDR3 includes enhancements that make it more robust with respect to high proper motion stars, and the disturbing effects of spurious and partially resolved sources. The source list is largely the same as that for Gaia DR2, but it does feature new sources and there are some notable changes. The source list will not change for Gaia DR3. Gaia EDR3 represents a significant advance over Gaia DR2, with parallax precisions increased by 30 percent, proper motion precisions increased by a factor of 2, and the systematic errors in the astrometry suppressed by 30--40 percent for the parallaxes and by a factor ~2.5 for the proper motions. The photometry also features increased precision, but above all much better homogeneity across colour, magnitude, and celestial position. A single passband for G, G_BP, and G_RP is valid over the entire magnitude and colour range, with no systematics above the 1 percent level.

Attackers may attempt exploiting Internet of Things (IoT) devices to operate them unduly as well as to gather personal data of the legitimate device owners'. Vulnerability Assessment and Penetration Testing (VAPT) sessions help to verify the effectiveness of the adopted security measures. However, VAPT over IoT devices, namely VAPT targeted at IoT devices, is an open research challenge due to the variety of target technologies and to the creativity it may require. Therefore, this article aims at guiding penetration testers to conduct VAPT sessions over IoT devices by means of a new cyber Kill Chain (KC) termed PETIoT. Several practical applications of PETIoT confirm that it is general, while its main novelty lies in the combination of attack and defence steps. PETIoT is demonstrated on a relevant example, the best-selling IP camera on Amazon Italy, the TAPO C200 by TP-Link, assuming an attacker who sits on the same network as the device's in order to assess all the network interfaces of the device. Additional knowledge is generated in terms of three zero-day vulnerabilities found and practically exploited on the camera, one of these with High severity and the other two with Medium severity by the CVSS standard. These are camera Denial of Service (DoS), motion detection breach and video stream breach. The application of PETIoT culminates with the proof-of-concept of a home-made fix, based on an inexpensive Raspberry Pi 4 Model B device, for the last vulnerability. Ultimately, our responsible disclosure with the camera vendor led to the release of a firmware update that fixes all found vulnerabilities, confirming that PetIoT has valid impact in real-world scenarios.

We present Alexa versus Alexa (AvA), a novel attack that leverages audio files containing voice commands and audio reproduction methods in an offensive fashion, to gain control of Amazon Echo devices for a prolonged amount of time. AvA leverages the fact that Alexa running on an Echo device correctly interprets voice commands originated from audio files even when they are played by the device itself -- i.e., it leverages a command self-issue vulnerability. Hence, AvA removes the necessity of having a rogue speaker in proximity of the victim's Echo, a constraint that many attacks share. With AvA, an attacker can self-issue any permissible command to Echo, controlling it on behalf of the legitimate user. We have verified that, via AvA, attackers can control smart appliances within the household, buy unwanted items, tamper linked calendars and eavesdrop on the user. We also discovered two additional Echo vulnerabilities, which we call Full Volume and Break Tag Chain. The Full Volume increases the self-issue command recognition rate, by doubling it on average, hence allowing attackers to perform additional self-issue commands. Break Tag Chain increases the time a skill can run without user interaction, from eight seconds to more than one hour, hence enabling attackers to setup realistic social engineering scenarios. By exploiting these vulnerabilities, the adversary can self-issue commands that are correctly executed 99% of the times and can keep control of the device for a prolonged amount of time. We reported these vulnerabilities to Amazon via their vulnerability research program, who rated them with a Medium severity score. Finally, to assess limitations of AvA on a larger scale, we provide the results of a survey performed on a study group of 18 users, and we show that most of the limitations against AvA are hardly used in practice.