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Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. Ideally, between these two extreme models lies a continuous range of models that adopt different levels of sequentiality. This paper proposes a graph generative model, called Insert-Fill-Halt (IFH), that supports the specification of a sequentiality degree. IFH is based upon the theory of Denoising Diffusion Probabilistic Models (DDPM), designing a node removal process that gradually destroys a graph. An insertion process learns to reverse this removal process by inserting arcs and nodes according to the specified sequentiality degree. We evaluate the performance of IFH in terms of quality, run time, and memory, depending on different sequentiality degrees. We also show that using DiGress, a diffusion-based one-shot model, as a generative step in IFH leads to improvement to the model itself, and is competitive with the current state-of-the-art., Comment: Accepted at the 27th European Conference on Artificial Intelligence (ECAI 24)

Schr\"{o}dinger cat states of levitated masses have several applications in sensing and, offer an avenue to explore the fundamental nature -- classical vs nonclassical -- of gravity, eg, through gravitationally induced entanglement (GIE). The interaction between a qubit and a levitated mass is a convenient method to create such a cat state. The size of the superpositions is limited by weak mass-qubit interactions. To overcome this limitation, we propose a protocol that exponentially expands an initially small superposition via Gaussian dynamics and successfully recombines it to complete an interferometry. An unknown force can be sensed by the superposition exponentially fast in the expansion time. The entanglement between two such interferometers interacting via a quantum force is -- for the first time in qubit-based non-Gaussian protocols -- obtained by solving the full quantum dynamics using Gaussian techniques. GIE grows exponentially, thereby making it closer to experimental feasibility. Requirements of experimental precision and decoherence are obtained., Comment: 5 Pages, 2 Figures, Table, and Appendix. Comments are welcome

United Nations set Sustainable Development Goals and this paper focuses on 7th (Affordable and Clean Energy), 9th (Industries, Innovation and Infrastructure), and 13th (Climate Action) goals. Climate change is a major concern in our society; for this reason, a current global objective is to reduce energy waste. This work summarizes all main approaches towards energy efficiency using Artificial Intelligence with a particular focus on multi-agent systems to create smart buildings. It mentions the tight relationship between AI, especially IoT, and Big Data. It explains the application of AI to anomaly detection in smart buildings and a possible classification of Intelligent Energy Management Systems: Direct and Indirect. Finally, some drawbacks of AI approaches and some possible future research focuses are proposed.

In this article we discuss a peculiar regime of Compton Scattering that assures the maximum transfer of energy and momentum from free electrons propagating in vacuum to the scattered photons. We name this regime Full Inverse Compton Scattering (FICS) because it is characterized by the maximum and full energy loss of the electrons in collision with photons: up to 100 % of the electron kinetic energy is indeed transferred to the photon. In the case of relativistic electrons, characterized by a large Lorentz factor (gamma >> 1), FICS regime corresponds to an incident photon energy equal to mec^2/2 . We interpret such an astonishing result as FICS being the time reversal of direct Compton Scattering of very energetic photons (of energy much greater than mec2) onto atomic electrons. Although the cross section of Compton scattering is decreasing with the energy of the incident photon, making the process less probable with respect to other reactions (pair production, nuclear reactions, etc) when high energetic photons are bombarding a target, the kinematics straightforwardly implies that the back-scattered photons would have an energy reaching asymptotically me^2c^2 . FICS is instead the unique suitable working point in Compton scattering for achieving the total transfer of (kinetic) energy exactly from the electron to the photon. Experiencing transitions from the initial momentum to zero in the laboratory system, in FICS the electron is also subject to very large negative acceleration; this fact can lead to possible experiments of sensing the Unruh temperature and related photon bath. On the other side of the energy dynamic range, low relativistic electrons can be completely stopped by moderate energy photons (tens of keV), leading to full exchange of temperature between electron clouds and photon baths.

Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.

The optical and radiochemical purification of the scintillating liquid, which will fill the central detector of the JUNO experiment, plays a crucial role in achieving its scientific goals. Given its gigantic mass and dimensions and an unprecedented target value of about 3% @ 1 MeV in energy resolution, JUNO has set severe requirements on the parameters of its scintillator, such as attenuation length (Lat>20 m at 430 nm), transparency, light yield, and content of radioactive contaminants (238U,232Th<10-15 g/g). To accomplish these needs, the scintillator will be processed using several purification methods, including distillation in partial vacuum and gas stripping, which are performed in two large scale plants installed at the JUNO site. In this paper, layout, operating principles, and technical aspects which have driven the design and construction of the distil- lation and gas stripping plants are reviewed. The distillation is effective in enhancing the optical properties and removing heavy radio-impurities (238U,232Th, 40K), while the stripping process exploits pure water steam and high-purity nitrogen to extract gaseous contaminants (222Rn, 39Ar, 85Kr, O2) from the scintillator. The plant operating parameters have been tuned during the recent com- missioning phase at the JUNO site and several QA/QC measurements and tests have been performed to evaluate the performances of the plants. Some preliminary results on the efficiency of these purification processes will be shown., Comment: 11 pages, 7 figures

Graph Neural Networks (GNNs) have gained significant attention in recent years due to their ability to learn representations of graph structured data. Two common methods for training GNNs are mini-batch training and full-graph training. Since these two methods require different training pipelines and systems optimizations, two separate categories of GNN training systems emerged, each tailored for one method. Works that introduce systems belonging to a particular category predominantly compare them with other systems within the same category, offering limited or no comparison with systems from the other category. Some prior work also justifies its focus on one specific training method by arguing that it achieves higher accuracy than the alternative. The literature, however, has incomplete and contradictory evidence in this regard. In this paper, we provide a comprehensive empirical comparison of full-graph and mini-batch GNN training systems to get a clearer picture of the state of the art in the field. We find that the mini-batch training systems we consider consistently converge faster than the full-graph training ones across multiple datasets, GNN models, and system configurations, with speedups between 2.4x - 15.2x. We also find that both training techniques converge to similar accuracy values, so comparing systems across the two categories in terms of time-to-accuracy is a sound approach., Comment: 12 pages, 1 appendix, 8 Figures, 16 Tables, Graph Neural Network, Graph Neural Networks, Full-graph training, Mini-batch training, full-batch training, distributed training, performance, epoch time, time to accuracy, accuracy

In the field of rare event physics, it is common to have huge masses of organic liquid scintillator as detection medium. In particular, they are widely used to study neutrino properties or astrophysical neutrinos. Thanks to its safety properties (such as low toxicity and high flash point) and easy scalability, linear alkyl benzene is the most common solvent used to produce liquid scintillators for large mass experiments. The knowledge of the refractive index is a pivotal point to understand the detector response, as this quantity (and its wavelength dependence) affects the Cherenkov radiation and photon propagation in the medium. In this paper, we report the measurement of the refractive index of the JUNO liquid scintillator between 260-1064 nm performed with two different methods (an ellipsometer and a refractometer), with a sub percent level precision. In addition, we used an interferometer to measure the group velocity in the JUNO liquid scintillator and verify the expected value derived from the refractive index measurements., Comment: 6 pages, 9 figures

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables

We initiate the study of counting Markov Equivalence Classes (MEC) under logical constraints. MECs are equivalence classes of Directed Acyclic Graphs (DAGs) that encode the same conditional independence structure among the random variables of a DAG model. Observational data can only allow to infer a DAG model up to Markov Equivalence. However, Markov equivalent DAGs can represent different causal structures, potentially super-exponentially many. Hence, understanding MECs combinatorially is critical to understanding the complexity of causal inference. In this paper, we focus on analysing MECs of size one, with logical constraints on the graph topology. We provide a polynomial-time algorithm (w.r.t. the number of nodes) for enumerating essential DAGs (the only members of an MEC of size one) with arbitrary logical constraints expressed in first-order logic with two variables and counting quantifiers (C^2). Our work brings together recent developments in tractable first-order model counting and combinatorics of MECs., Comment: Under Review

We show that the sign of the Euler characteristic of an $S$-arithmetic subgroup of a simple $k$-group over a number field $k$ depends on the $S$-congruence completion only. Consequently, the sign is a profinite invariant for such $S$-arithmetic groups with the congruence subgroup property. This generalizes previous work of the first author with Kionke-Raimbault-Sauer., Comment: 19 pages. Rearranged introduction

We revisit the kinematics of Compton Scattering (electron-photon interactions producing electrons and photons in the exit channel) covering the full range of energy/momenta distribution between the two colliding particles, with a dedicated view to statistical properties of secondary beams that are generated in beam-beam collisions. Starting from the Thomson inverse scattering, where electrons do not recoil and photons are back-scattered to higher energies by a Lorentz boost effect (factor $4\gamma^2$), we analyze three transition points, separating four regions. These are in sequence, given by increasing the electron recoil (numbers are for transition points, letters for regions): a) Thomson back-scattering, 1) equal sharing of total energy in the exit channel between electron and photon, b) deep recoil regime where the bandwidth/energy spread of the two interacting beams are exchanged in the exit channel, 2) electron is stopped, i.e. taken down at rest in the laboratory system by colliding with an incident photon of $mc^2/2$ energy, c) electron back-scattering region, where incident electron is back-scattered by the incident photon, 3) symmetric scattering, when the incident particles carry equal and opposite momenta, so that in the exit channel they are back-scattered with same energy/momenta, d) Compton scattering ($a'$ $la$ Arthur Compton, see ref.4), where photons carry an energy much larger than the colliding electron energy. For each region and/or transition point we discuss the potential effects of interest in diverse areas, like generating mono-chromatic gamma ray beams in deep recoil regions with spectral purification, or possible mechanisms of generation and propagation of very high energy photons in the cosmological domain.

We present a novel prior for tree topology within Bayesian Additive Regression Trees (BART) models. This approach quantifies the hypothetical loss in information and the loss due to complexity associated with choosing the wrong tree structure. The resulting prior distribution is compellingly geared toward sparsity, a critical feature considering BART models' tendency to overfit. Our method incorporates prior knowledge into the distribution via two parameters that govern the tree's depth and balance between its left and right branches. Additionally, we propose a default calibration for these parameters, offering an objective version of the prior. We demonstrate our method's efficacy on both simulated and real datasets.

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)

Graph pattern matching is a fundamental problem encountered by many common graph mining tasks and the basic building block of several graph mining systems. This paper explores for the first time how to proactively prune graphs to speed up graph pattern matching by leveraging the structure of the query pattern and the input graph. We propose building auxiliary graphs, which are different pruned versions of the graph, during query execution. This requires careful balancing between the upfront cost of building and managing auxiliary graphs and the gains of faster set operations. To this end, we propose GraphMini, a new system that uses query compilation and a new cost model to minimize the cost of building and maintaining auxiliary graphs and maximize gains. Our evaluation shows that using GraphMini can achieve one order of magnitude speedup compared to state-of-the-art subgraph enumeration systems on commonly used benchmarks.

Deep Learning (DL) techniques have achieved remarkable successes in recent years. However, their ability to generalize and execute reasoning tasks remains a challenge. A potential solution to this issue is Neuro-Symbolic Integration (NeSy), where neural approaches are combined with symbolic reasoning. Most of these methods exploit a neural network to map perceptions to symbols and a logical reasoner to predict the output of the downstream task. These methods exhibit superior generalization capacity compared to fully neural architectures. However, they suffer from several issues, including slow convergence, learning difficulties with complex perception tasks, and convergence to local minima. This paper proposes a simple yet effective method to ameliorate these problems. The key idea involves pretraining a neural model on the downstream task. Then, a NeSy model is trained on the same task via transfer learning, where the weights of the perceptual part are injected from the pretrained network. The key observation of our work is that the neural network fails to generalize only at the level of the symbolic part while being perfectly capable of learning the mapping from perceptions to symbols. We have tested our training strategy on various SOTA NeSy methods and datasets, demonstrating consistent improvements in the aforementioned problems., Comment: Accepted as full paper at the International Conference on Neural-Symbolic Learning and Reasoning (NeSy 2024)

We find the entanglement entropy of a subregion of the vacuum state in scalar quantum electrodynamics, working perturbatively to the 2-loops level. Doing so leads us to derive the Maxwell-Proca propagator in conical Euclidean space. The area law of entanglement entropy is recovered in both the massive and massless limits of the theory, as is expected. These results yield the renormalisation group flow of entanglement entropy, and we find that loop contributions suppress entanglement entropy. We highlight these results in the light of the renormalization group flow of couplings and correlators, which are increased in scalar quantum electrodynamics, so that the potential tension between the increase in correlations between two points of spacetime and the decrease in entanglement entropy between two regions of spacetime with energy is discussed. We indeed show that the vacuum of a subregion of spacetime purifies with energy in scalar quantum electrodynamics, which is related to the concept of screening., Comment: 10 pages, 1 figure

The numerical study of relativistic magnetohydrodynamics (MHD) plays a crucial role in high-energy astrophysics, but unfortunately is computationally demanding, given the complex physics involved (high Lorentz factor flows, extreme magnetization, curved spacetimes near compact objects) and the large variety of spatial scales needed to resolve turbulent motions. A great benefit comes from the porting of existing codes running on standard processors to GPU-based platforms. However, this usually requires a drastic rewriting of the original code, the use of specific languages like CUDA, and a complex analysis of data management and optimization of parallel processes. Here we describe the porting of the ECHO code for special and general relativistic MHD to accelerated devices, simply based on native Fortran language built-in constructs, especially 'do concurrent' loops, few OpenACC directives, and the straightforward data management provided by the Unified Memory option of NVIDIA compilers.Thanks to these very minor modifications to the original code, the new version of ECHO runs at least 16 times faster on GPU platforms compared to CPU-based ones. The chosen benchmark is the 3D propagation of a relativistic MHD Alfv\'en wave, for which strong and weak scaling tests performed on the LEONARDO pre-exascale supercomputer at CINECA are provided (using up to 256 nodes corresponding to 1024 GPUs, and over 14 billion cells). Finally, an example of high-resolution relativistic MHD Alfv\'enic turbulence simulation is shown, demonstrating the potential for astrophysical plasmas of the new GPU-based version of ECHO., Comment: Accepted for publication on Fluids, MDPI, 17 pages

Network is a major bottleneck in modern cloud databases that adopt a storage-disaggregation architecture. Computation pushdown is a promising solution to tackle this issue, which offloads some computation tasks to the storage layer to reduce network traffic. Existing cloud OLAP systems statically decide whether to push down computation during the query optimization phase and do not consider the storage layer's computational capacity and load. Besides, there is a lack of a general principle that determines which operators are amenable for pushdown. Existing systems design and implement pushdown features empirically, which ends up picking a limited set of pushdown operators respectively. In this paper, we first design Adaptive pushdown as a new mechanism to avoid throttling the storage-layer computation during pushdown, which pushes the request back to the computation layer at runtime if the storage-layer computational resource is insufficient. Moreover, we derive a general principle to identify pushdown-amenable computational tasks, by summarizing common patterns of pushdown capabilities in existing systems. We propose two new pushdown operators, namely, selection bitmap and distributed data shuffle. Evaluation results on TPC-H show that Adaptive pushdown can achieve up to 1.9x speedup over both No pushdown and Eager pushdown baselines, and the new pushdown operators can further accelerate query execution by up to 3.0x., Comment: 13 pages, 15 figures

Recently, there has been a proposal to test the quantum nature of gravity in the laboratory by witnessing the growth of entanglement between two masses in spatial quantum superpositions. The required superpositions can be created via Stern-Gerlach interferometers, which couple an embedded spin qubit quantum state to the spatial dynamics of each mass. The masses would entangle only if gravity is quantum in nature. Here, we generalise the experiment to an arbitrary spin $j$ or equivalently to an ensemble of uniformly coupled spins. We first exemplify how to create a generalized Stern-Gerlach interferometer, which splits the mass into $2j+1$ trajectories. This shows that a controlled protocol can be formulated to encode the amplitudes of any spin state to a spatial superposition. Secondly, two masses in spatial superpositions of the above form are left to interact via gravity, and the entanglement is computed. Different families of initial spin states are varied to find the optimal spin state that maximizes the entanglement. We conclude that larger spins can offer a modest advantage in enhancing gravity-induced entanglement., Comment: 12+7 pages, 10 figures. Comments are welcome

Nuclear reactors are a source of electron antineutrinos due to the presence of unstable fission products that undergo $\beta^-$ decay. They will be exploited by the JUNO experiment to determine the neutrino mass ordering and to get very precise measurements of the neutrino oscillation parameters. This requires the reactor antineutrino spectrum to be characterized as precisely as possible both through high resolution measurements, as foreseen by the TAO experiment, and detailed simulation models. In this paper we present a benchmark analysis utilizing Serpent Monte Carlo simulations in comparison with real pressurized water reactor spent fuel data. Our objective is to study the accuracy of fission fraction predictions as a function of different reactor simulation approximations. Then, utilizing the BetaShape software, we construct fissile antineutrino spectra using the summation method, thereby assessing the influence of simulation uncertainties on reactor antineutrino spectrum.

The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN., Comment: 24 pages, 9 figures, accepted for the publication at JCAP

We consider scattering processes where a quantum system is comprised of an inner subsystem and of a boundary, and is subject to Haar-averaged random unitaries acting on the boundary-environment Hilbert space only. We show that, regardless of the initial state, a single scattering event will disentangle the unconditional state (i.e., the scattered state when no information about the applied unitary is available) across the inner subsystem-boundary partition. Also, we apply Levy's lemma to constrain the trace norm fluctuations around the unconditional state. Finally, we derive analytical formulae for the mean scattered purity for initial globally pure states, and provide one with numerical evidence of the reduction of fluctuations around such mean values with increasing environmental dimension., Comment: 12 pages (5 pages of main text + 7 pages of appendices)

Weighted First Order Model Counting (WFOMC) is fundamental to probabilistic inference in statistical relational learning models. As WFOMC is known to be intractable in general ($\#$P-complete), logical fragments that admit polynomial time WFOMC are of significant interest. Such fragments are called domain liftable. Recent works have shown that the two-variable fragment of first order logic extended with counting quantifiers ($\mathrm{C^2}$) is domain-liftable. However, many properties of real-world data, like acyclicity in citation networks and connectivity in social networks, cannot be modeled in $\mathrm{C^2}$, or first order logic in general. In this work, we expand the domain liftability of $\mathrm{C^2}$ with multiple such properties. We show that any $\mathrm{C^2}$ sentence remains domain liftable when one of its relations is restricted to represent a directed acyclic graph, a connected graph, a tree (resp. a directed tree) or a forest (resp. a directed forest). All our results rely on a novel and general methodology of "counting by splitting". Besides their application to probabilistic inference, our results provide a general framework for counting combinatorial structures. We expand a vast array of previous results in discrete mathematics literature on directed acyclic graphs, phylogenetic networks, etc., Comment: Under Review at the Artificial Intelligence Journal. arXiv admin note: text overlap with arXiv:2302.09830

The formation of the Earth remains an epoch with mysterious puzzles extending to our still incomplete understanding of the planet's potential origin and bulk composition. Direct confirmation of the Earth's internal heat engine was accomplished by the successful observation of geoneutrinos originating from uranium (U) and thorium (Th) progenies, manifestations of the planet's natural radioactivity dominated by potassium (40K) and the decay chains of uranium (238U) and thorium (232Th). This radiogenic energy output is critical to planetary dynamics and must be accurately measured for a complete understanding of the overall heat budget and thermal history of the Earth. Detecting geoneutrinos remains the only direct probe to do so and constitutes a challenging objective in modern neutrino physics. In particular, the intriguing potassium geoneutrinos have never been observed and thus far have been considered impractical to measure. We propose here a novel approach for potassium geoneutrino detection using the unique antimatter signature of antineutrinos to reduce the otherwise overwhelming backgrounds to observing this rarest signal. The proposed detection framework relies on the innovative LiquidO detection technique to enable positron (e+) identification and antineutrino interactions with ideal isotope targets identified here for the first time. We also provide the complete experimental methodology to yield the first potassium geoneutrino discovery.

Snoring is a common disorder that affects people's social and marital lives. The annoyance caused by snoring can be partially solved with active noise control systems. In this context, the present work aims at introducing an enhanced system based on the use of a convolutional recurrent neural network for snoring activity detection and a delayless subband approach for active snoring cancellation. Thanks to several experiments conducted using real snoring signals, this work shows that the active snoring cancellation system achieves better performance when the snoring activity detection stage is turned on, demonstrating the beneficial effect of a preliminary snoring detection stage in the perspective of snoring cancellation.

Packet loss is a major cause of voice quality degradation in VoIP transmissions with serious impact on intelligibility and user experience. This paper describes a system based on a generative adversarial approach, which aims to repair the lost fragments during the transmission of audio streams. Inspired by the powerful image-to-image translation capability of Generative Adversarial Networks (GANs), we propose bin2bin, an improved pix2pix framework to achieve the translation task from magnitude spectrograms of audio frames with lost packets, to noncorrupted speech spectrograms. In order to better maintain the structural information after spectrogram translation, this paper introduces the combination of two STFT-based loss functions, mixed with the traditional GAN objective. Furthermore, we employ a modified PatchGAN structure as discriminator and we lower the concealment time by a proper initialization of the phase reconstruction algorithm. Experimental results show that the proposed method has obvious advantages when compared with the current state-of-the-art methods, as it can better handle both high packet loss rates and large gaps., Comment: Accepted at EUSIPCO - 31st European Signal Processing Conference, 2023

Half-metallic half-Heusler compounds with strong spin-orbit-coupling and broken inversion symmetry in their crystal structure are promising materials for generating and absorbing spin-currents, thus enabling the electric manipulation of magnetization in energy-efficient spintronic devices. In this work, we report the spin-to-charge conversion in sputtered ultrathin films of the half-Heusler compound MnPtSb with thickness (t) in the range from 1 to 6 nm. A combination of X-ray and transmission electron microscopy measurements evidence the epitaxial nature of these ultrathin non-centrosymmetric MnPtSb films, with a clear (111)-orientation obtained on top of (0001) single-crystal sapphire substrates. The study of the thickness (t)-dependent magnetization dynamics of the MnPtSb(t)/Co(5nm)/Au(5nm) heterostructure revealed that the MnPtSb compound can be used as an efficient spin current generator, even at film thicknesses as low as 1 nm. By making use of spin pumping FMR, we measure a remarkable t-dependent spin-charge conversion in the MnPtSb layers, which clearly demonstrate the interfacial origin of the conversion. When interpreted as arising from the inverse Edelstein effect (IEE), the spin-charge conversion efficiency extracted at room temperature for the thinnest MnPtSb layer reaches {\lambda}IEE~3 nm, representing an extremely high spin-charge conversion efficiency at room temperature. The still never explored ultrathin regime of the MnPtSb films studied in this work and the discover of their outstanding functionality are two ingredients which demonstrate the potentiality of such materials for future applications in spintronics., Comment: main text: 17 pages (with 4 figures), plus additional 8 pages of Supplementary Information