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This report summarizes the outcomes of the ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation (MSLesSeg). The competition aimed to develop methods capable of automatically segmenting multiple sclerosis lesions in MRI scans. Participants were provided with a novel annotated dataset comprising a heterogeneous cohort of MS patients, featuring both baseline and follow-up MRI scans acquired at different hospitals. MSLesSeg focuses on developing algorithms that can independently segment multiple sclerosis lesions of an unexamined cohort of patients. This segmentation approach aims to overcome current benchmarks by eliminating user interaction and ensuring robust lesion detection at different timepoints, encouraging innovation and promoting methodological advances.

The analysis of a crime scene is a pivotal activity in forensic investigations. Crime Scene Investigators and forensic science practitioners rely on best practices, standard operating procedures, and critical thinking, to produce rigorous scientific reports to document the scenes of interest and meet the quality standards expected in the courts. However, crime scene examination is a complex and multifaceted task often performed in environments susceptible to deterioration, contamination, and alteration, despite the use of contact-free and non-destructive methods of analysis. In this context, the documentation of the sites, and the identification and isolation of traces of evidential value remain challenging endeavours. In this paper, we propose a photogrammetric reconstruction of the crime scene for inspection in virtual reality (VR) and focus on fully automatic object recognition with deep learning (DL) algorithms through a client-server architecture. A pre-trained Faster-RCNN model was chosen as the best method that can best categorize relevant objects at the scene, selected by experts in the VR environment. These operations can considerably improve and accelerate crime scene analysis and help the forensic expert in extracting measurements and analysing in detail the objects under analysis. Experimental results on a simulated crime scene have shown that the proposed method can be effective in finding and recognizing objects with potential evidentiary value, enabling timely analyses of crime scenes, particularly those with health and safety risks (e.g. fires, explosions, chemicals, etc.), while minimizing subjective bias and contamination of the scene.

We present 3D general-relativistic neutrino-radiation hydrodynamics simulations of asymmetric binary neutron star mergers producing long-lived neutron stars remnants and spanning a fraction of their cooling time scale. Two binaries with mass ratios of $1.77$ and $1.49$ described by a stiff and a soft microphysical equation of state are considered. The mergers are characterized by significant tidal disruption with neutron rich material forming a massive disc around the remnant. The latter develops a one-armed dynamics that is imprinted in the emitted kilo-Hertz gravitational waves. Angular momentum transport to the disc is initially driven by spiral-density waves and enhanced by turbulent viscosity and neutrino heating on longer timescales. The mass outflows are composed by neutron-rich dynamical ejecta of mass ${\sim}10^{-3}-10^{-2}M_\odot$ followed by a persistent spiral-wave/neutrino-driven wind of ${\gtrsim}10^{-2}M_\odot$ with material spanning a wide range of electron fractions, ${\sim}0.1-0.55$. For the stiff EOS and largest mass ratio binary, tidal dynamical ejecta have fast tails up to velocities ${\sim}0.8$~c. The outflows are further evolved to days timescale using 2D ray-by-ray radiation-hydrodynamics simulations that include an online nuclear network. We find complete $r$-process yields and identify the production of $^{56}$Ni and the subsequent decay chain to $^{56}$Co and $^{56}$Fe. Synthetic kilonova light curves predict an extended (near-) infrared peak a few days postmerger originating from $r$-processes in the neutron-rich/high-opacity ejecta and a UV/optical peak a few hours postmerger originating from weak $r$-processes in the faster ejecta components. Additionally, the fast tail of tidal origin generates kilonova afterglows potentially detectable in radio and X band on a few to ten years timescale. (Abridged), Comment: 17 pages, 15 figures, 2 appendices

Observational studies showed that galaxy disks are already in place in the first few billion years of the universe. The early disks detected so far, with typical half-light radii of 3 kiloparsecs at stellar masses around 10^11 M_sun for redshift z~3, are significantly smaller than today's disks with similar masses, in agreement with expectations from current galaxy models. Here, we report observations of a giant disk at z=3.25, when the universe was only 2 billion years old, with a half-light radius of 9.6 kiloparsecs and stellar mass of 3.7^+2.6_-2.2x10^11 M_sun. This galaxy is larger than any other kinematically-confirmed disks at similar epochs and surprisingly similar to today's largest disks regarding size and mass. JWST imaging and spectroscopy reveal its spiral morphology and a rotational velocity consistent with local Tully-Fisher relation. Multi-wavelength observations show that it lies in an exceptionally dense environment, where the galaxy number density is over ten times higher than the cosmic average and mergers are frequent. The discovery of such a giant disk suggests the presence of favorable physical conditions for large-disk formation in dense environments in the early universe, which may include efficient accretion of gas carrying coherent angular momentum and non-destructive mergers between exceptionally gas-rich progenitor galaxies., Comment: 22 pages, 11 figures; submitted

We present a detailed study of the kinematics of OVI-bearing gas around 60 low-mass (median log(M*/Msun)~8.9) galaxies at low redshift (0.1 < z < 0.7) using background quasars (median impact parameter $\approx115$ kpc) as part of the MUSE Quasar-fields Blind Emitters Survey (MUSEQuBES). We find that the majority of the OVI absorbers detected within the virial radius have line-of-sight velocities smaller than the escape velocities and are thus consistent with being gravitationally bound, irrespective of the halo mass. However, the fraction of such absorbers declines at larger impact parameters. The Doppler $b$ parameter and the velocity width ($\Delta v_{90}$) of the OVI absorbers exhibit large scatter inside the virial radius of the host galaxies, but the scatter declines sharply at impact parameter $D \gtrsim 2R_{\rm vir}$. For high-mass galaxies (log(M*/Msun)>9), OVI absorption displays a larger kinematic spread, quantified by the pixel-velocity two-point correlation function (TPCF). However, this difference disappears for the isolated galaxies when the pixel velocities are scaled by the galaxy's circular velocity. We do not find any significant difference between the TPCF of isolated and group galaxies when the stellar mass is controlled for. A significant fraction of groups (4/6) with four or more member galaxies do not show any detectable OVI absorption, likely due to the passive nature of nearest galaxies., Comment: Submitted to ApJ. Comments are welcome

We present a study of OVI-bearing gas around 247 low-mass (median log(M*/Msun)~8.7) galaxies at low redshifts (0.1 < z < 0.7) using background quasars as part of the MUSE Quasar-fields Blind Emitters Survey (MUSEQuBES). We find that the average OVI column density, ${\rm log}_{10}$ = $14.14^{+0.09}_{-0.10}$, measured within the virial radius for our sample, is significantly lower than for L_* galaxies. Combining 253 star-forming galaxies (mostly more massive) from the literature with 176 star-forming galaxies from MUSEQuBES, we find that both and the average covering fraction peak at log(M*/Msun)~9.5. The virial temperature corresponding to this stellar mass is ideal for OVI production via collisional ionization. However, we argue that photoionization and/or non-equilibrium processes are necessary to produce the OVI associated with low-mass, dwarf galaxies (log(M*/Msun)<9). The average OVI mass within the virial radius of dwarf galaxies is measured to be $10^{5.2_{-0.1}^{+0.1}}$ Msun. The characteristic normalized impact parameter at which the OVI covering fraction drops to half of its peak value is the largest (~1.1) for galaxies with stellar mass log(M*/Msun)~9.5. We report the presence of a highly ionized metal floor with ${\rm log}_{10}(N({\rm OVI})/{\rm cm}^{-2}) = 13.2$ outside the virial radius of dwarf galaxies inferred from median spectral stacking., Comment: Submitted to ApJ. Comments are welcome

Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.

Studying the orbital motion of stars around Sagittarius A* in the Galactic Center provides a unique opportunity to probe the gravitational potential near the supermassive black hole at the heart of our Galaxy. Interferometric data obtained with the GRAVITY instrument at the Very Large Telescope Interferometer (VLTI) since 2016 has allowed us to achieve unprecedented precision in tracking the orbits of these stars. GRAVITY data have been key to detecting the in-plane, prograde Schwarzschild precession of the orbit of the star S2, as predicted by General Relativity. By combining astrometric and spectroscopic data from multiple stars, including S2, S29, S38, and S55 - for which we have data around their time of pericenter passage with GRAVITY - we can now strengthen the significance of this detection to an approximately $10 \sigma$ confidence level. The prograde precession of S2's orbit provides valuable insights into the potential presence of an extended mass distribution surrounding Sagittarius A*, which could consist of a dynamically relaxed stellar cusp comprised of old stars and stellar remnants, along with a possible dark matter spike. Our analysis, based on two plausible density profiles - a power-law and a Plummer profile - constrains the enclosed mass within the orbit of S2 to be consistent with zero, establishing an upper limit of approximately $1200 \, M_\odot$ with a $1 \sigma$ confidence level. This significantly improves our constraints on the mass distribution in the Galactic Center. Our upper limit is very close to the expected value from numerical simulations for a stellar cusp in the Galactic Center, leaving little room for a significant enhancement of dark matter density near Sagittarius A*., Comment: Submitted to A&A on September 17, 2024

We introduce an extension to the AthenaK code for general-relativistic magnetohydrodynamics (GRMHD) in dynamical spacetimes using a 3+1 conservative Eulerian formulation. Like the fixed-spacetime GRMHD solver, we use standard finite-volume methods to evolve the fluid and a constrained transport scheme to preserve the divergence-free constraint for the magnetic field. We also utilize a first-order flux correction (FOFC) scheme to reduce the need for an artificial atmosphere and optionally enforce a maximum principle to improve robustness. We demonstrate the accuracy of AthenaK using a set of standard tests in flat and curved spacetimes. Using a SANE accretion disk around a Kerr black hole, we compare the new solver to the existing solver for stationary spacetimes using the so-called "HARM-like" formulation. We find that both formulations converge to similar results. We also include the first published binary neutron star (BNS) mergers performed on graphical processing units (GPUs). Thanks to the FOFC scheme, our BNS mergers maintain a relative error of $\mathcal{O}(10^{-11})$ or better in baryon mass conservation up to collapse. Finally, we perform scaling tests of AthenaK on OLCF Frontier, where we show excellent weak scaling of $\geq 80\%$ efficiency up to 32768 GPUs and $74\%$ up to 65536 GPUs for a GRMHD problem in dynamical spacetimes with six levels of mesh refinement. AthenaK achieves an order-of-magnitude speedup using GPUs compared to CPUs, demonstrating that it is suitable for performing numerical relativity problems on modern exascale resources., Comment: 26 pages, 25 figures; to be submitted to ApJS soon

We present the numerical relativity module within AthenaK, an open source performance-portable astrophysics code designed for exascale computing applications. This module employs the Z4c formulation to solve the Einstein equations. We demonstrate its accuracy through a series of standard numerical relativity tests, including convergence of the gravitational waveform from binary black hole coalescence. Furthermore, we conduct scaling tests on OLCF Frontier and NERSC Perlmutter, where AthenaK exhibits excellent weak scaling efficiency of 80% on up to 65,536 AMD MI250X GPUs on Frontier (relative to 4 GPUs) and strong scaling efficiencies of 84% and 77% on AMD MI250X and NVIDIA A100 GPUs on Frontier and Perlmutter respectively. Additionally, we observe a significant performance boost, with two orders of magnitude speedup ($\gtrsim 200\times$) on a GPU compared to a single CPU core, affirming that AthenaK is well-suited for exascale computing, thereby expanding the potential for breakthroughs in numerical relativity research.

We give an equivalence of categories between: (i) M\"obius vertex algebras which are equipped with a choice of generating family of quasiprimary vectors, and (ii) (not-necessarily-unitary) M\"obius-covariant Wightman conformal field theories on the unit circle. We do not impose any technical restrictions on the theories considered (such as finite-dimensional conformal weight spaces or simplicity), yielding the most general equivalence between these two axiomatizations of two-dimensional chiral conformal field theory. This provides new opportunities to study non-unitary vertex algebras using the lens of algebraic conformal field theory and operator algebras, which we demonstrate by establishing a non-unitary version of the Reeh-Schlieder theorem., Comment: 45 pages

Quasi-periodic eruptions (QPEs) are an extreme X-ray variability phenomenon associated with low-mass supermassive black holes. First discovered in the nucleus of the galaxy GSN 069, they have been so far securely detected in five other galaxies, including RX J1301.9+2747. When detected, the out-of-QPE emission (quiescence) is consistent with the high-energy tail of thermal emission from an accretion disk. We present the X-ray and radio properties of RX J1301.9+2747, both in quiescence and during QPEs. We analyse X-ray data taken during five XMM-Newton observations between 2000 and 2022. The last three observations were taken in coordination with radio observations with the Karl G. Jansky Very Large Array. We also make use of EXOSAT, ROSAT, and Chandra archival observations taken between 1983 and 2009. XMM-Newton detected 34 QPEs of which 8 have significantly lower amplitudes than the others. No correlated radio/X-ray variability was observed during QPEs. In terms of timing properties, the QPEs in RX J1301.9+2747 do not exhibit the striking regularity observed in the discovery source GSN 069. In fact there is no clear repetition pattern between QPEs: the average time separation between their peaks is about four hours, but it can be as short as one, and as long as six hours. The QPE spectral properties of RX J1301.9+2747 as a function of energy are however very similar to those of GSN 069 and of other QPE sources. The quiescent emission of RX J1301.9+2747 is more complex than that of GSN 069, as it requires a soft X-ray excess-like component in addition to the thermal emission from the accretion disk. Its long-term X-ray quiescent flux variations are of low-amplitude and not strictly monotonic, with a general decay over $\sim 22$ years. We discuss our observational results in terms of some of the ideas and models that have been proposed so far for the physical origin of QPEs., Comment: Accepted for publication in Astronomy & Astrophysics. 22 pages and 17 figures in the main text; 11 pages, 5 tables, and 4 figures in the Appendix. Abstract shortened to comply with arXiv requirements

Many complex systems in science and engineering are modeled as networks whose nodes and links depict the temporal evolution of each system unit and the dynamic interaction between pairs of units, which are assessed respectively using measures of auto- and cross-correlation or variants thereof. However, a growing body of work is documenting that this standard network representation can neglect potentially crucial information shared by three or more dynamic processes in the form of higher-order interactions (HOIs). While several measures, mostly derived from information theory, are available to assess HOIs in network systems mapped by multivariate time series, none of them is able to provide a compact and detailed representation of higher-order interdependencies. In this work, we fill this gap by introducing a framework for the assessment of HOIs in dynamic network systems at different levels of resolution. The framework is grounded on the dynamic implementation of the O-information, a new measure assessing HOIs in dynamic networks, which is here used together with its local counterpart and its gradient to quantify HOIs respectively for the network as a whole, for each link, and for each node. The integration of these measures into the conventional network representation results in a tool for the representation of HOIs as networks, which is defined formally using measures of information dynamics, implemented in its linear version by using vector regression models and statistical validation techniques, illustrated in simulated network systems, and finally applied to an illustrative example in the field of network physiology.

Shock breakout emission is among the first observable signals in a wide variety of astrophysical phenomena, including neutron star (NS) mergers, and it can be the dominant component in low-luminosity short gamma-ray bursts (llsGRBs), as exemplified by GRB 170817A. In this work, we investigate the cocoon shock breakout emission in NS mergers and how its signal depends on the outermost layers of the ejecta profile, which we derive from general relativistic radiation hydrodynamic simulations. We study the formation of the cocoon as a consequence of a relativistic jet propagating through the ejecta. To explore the influence of the outermost layers of the ejecta on the breakout emission, we explore cases where the ejecta has a sharp cutoff or an extended smooth tail. We find that the shock breakout emission is strongly influenced by the shape of the ejecta outer layers, with extended tails yielding results consistent with the observed properties of GRB 170817A, whereas sharp cutoffs overestimate the radiated energy. Using a Bayesian analysis, we estimate the best fit parameters of the central engine, considering both accreting black hole and magnetized neutron star scenarios. Our findings indicate a slight preference for the scenarios where the engine is a black hole. Our work probes the nature of neutron star mergers and highlights the importance of the shape of the ejecta profile in modeling early electromagnetic counterparts to these mergers., Comment: 26 pages, 11 figures, submitted to journal

Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neural information processing. Information storage (IS), the main measure quantifying the active utilization of memory in a dynamic system, is only defined for discrete-time processes. While recent theoretical work laid the foundations for the continuous-time analysis of the predictive capacity stored in a process, methods for the effective computation of the related measures are needed to favor widespread utilization on neural data. This work introduces a method for the model-free estimation of the so-called memory utilization rate (MUR), the continuous-time counterpart of the IS, specifically designed to quantify the predictive capacity stored in neural point processes. The method employs nearest-neighbor entropy estimation applied to the inter-spike intervals measured from point-process realizations to quantify the extent of memory used by a spike train. An empirical procedure based on surrogate data is implemented to compensate the estimation bias and detect statistically significant levels of memory. The method is validated in simulated Poisson processes and in realistic models of coupled cortical dynamics and heartbeat dynamics. It is then applied to real spike trains reflecting central and autonomic nervous system activities: in spontaneously growing cortical neuron cultures, the MUR detected increasing memory utilization across maturation stages, associated to emergent bursting synchronized activity; in the study of the neuro-autonomic modulation of human heartbeats, the MUR reflected the sympathetic activation occurring with postural but not with mental stress. The proposed approach offers a computationally reliable tool to analyze spike train data in computational neuroscience and physiology.

This paper presents a detailed study of the physical properties of seven C IV absorbers identified at z_abs = 0.68-1.28 along the line of sight toward QSO PG 1522+101 (z_QSO = 1.330). The study leverages high-quality QSO spectra from HST COS and STIS, and Keck HIRES to resolve component structures and to constrain the gas density and elemental abundances of individual components. Under the assumption of photoionization equilibrium (PIE), five of the 12 C IV components require a mixture of high- and low-density phases to fully explain the observed relative abundances between low-, intermediate-, and high-ionization species. In addition, galaxy surveys carried out using VLT MUSE and Magellan LDSS3C are utilized to characterize the galaxy environments. The results of this analysis are summarized as follows: (1) no luminous galaxies (> 0.1 L*) are found within 100 kpc in projected distance from the C IV absorbers; (2) the C IV selection preferentially targets high-metallicity (near solar) and chemically-evolved gas (~ solar [C/O] elemental abundances) in galaxy halos; (3) the observed narrow line widths of individual C IV components, places a stringent limit on the gas temperature (< 5e4 K) and supports a photoionization origin; (4) additional local ionizing sources beyond the UV ionizing background may be necessary for at least one absorber based on the observed deficit of He I relative to H I; and (5) a PIE assumption may not apply when the gas metallicity exceeds the solar value and the component line width implies a warmer temperature than expected from PIE models., Comment: 31 pages, 16 figures, accepted to The Open Journal of Astrophysics with revisions

Open vocabulary 3D object detection (OV3D) allows precise and extensible object recognition crucial for adapting to diverse environments encountered in assistive robotics. This paper presents OpenNav, a zero-shot 3D object detection pipeline based on RGB-D images for smart wheelchairs. Our pipeline integrates an open-vocabulary 2D object detector with a mask generator for semantic segmentation, followed by depth isolation and point cloud construction to create 3D bounding boxes. The smart wheelchair exploits these 3D bounding boxes to identify potential targets and navigate safely. We demonstrate OpenNav's performance through experiments on the Replica dataset and we report preliminary results with a real wheelchair. OpenNav improves state-of-the-art significantly on the Replica dataset at mAP25 (+9pts) and mAP50 (+5pts) with marginal improvement at mAP. The code is publicly available at this link: https://github.com/EasyWalk-PRIN/OpenNav., Comment: ECCVW

We present intial results associating galaxies in the MUSE Ultra Deep Field (MUDF) with gas seen in absorption along the line-of-sight to two bright quasars in this field, to explore the dependence of metals in the circumgalactic medium (CGM) on galaxy properties. The MUDF includes $\sim$140h of VLT/MUSE data and 90 orbits of HST/G141M grism observations alongside VLT/UVES spectroscopy of the two quasars and several bands of HST imaging. We compare the metal absorption around galaxies in this field as a function of impact parameter, azimuthal angle and galaxy metallicity across redshifts 0.5 $<$ z $<$ 3.2. Due to the depth of our data and a large field-of-view, our analysis extends to low stellar masses ($<$ $10^{7}$ M$_{\odot}$) and high impact parameters ($>$ 600 kpc). We find a correlation between absorber equivalent width and number of nearby galaxies, but do not detect a significant anti-correlation with impact parameter. Our full sample does not show any significant change in absorber incidence as a function of azimuthal angle. However, we do find a bimodality in the azimuthal angle distribution of absorption at small impact parameters ($<$2 r$_{vir}$) and around highly-star-forming galaxies, possibly indicating disk-like accretion and biconical outflows. Finally, we do not detect any systematic deviation from the fundamental metallicity relation (FMR) among galaxies with detected absorption. This work is limited by gaps in the wavelength coverage of our current data; broader-wavelength observations with JWST will allow us to unlock the full potential of the MUDF for studying the CGM., Comment: Accepted for publication in The Astrophysical Journal. 31 pages, 17 figures

The shallow potential wells of star-forming dwarf galaxies make their surrounding circumgalactic and intergalactic medium (CGM/IGM) sensitive laboratories for studying the inflows and outflows thought to regulate galaxy evolution. We present new absorption-line measurements in quasar sightlines probing within projected distances of $<300$ kpc from 91 star-forming field dwarf galaxies with a median stellar mass of $\log{M_\star/\rm{M_\odot}} \approx 8.3$ at $0.077 < z < 0.73$ from the Cosmic Ultraviolet Baryon Survey (CUBS). In this redshift range, the CUBS quasar spectra cover a suite of transitions including H I, low and intermediate metal ions (e.g., C II, Si II, C III, and Si III), and highly ionized O VI. This CUBS-Dwarfs survey enables constraints with samples 9$\times$ larger than past dwarf CGM/IGM studies with similar ionic coverage. We find that low and intermediate ionization metal absorption is rare around dwarf galaxies, consistent with previous surveys of local dwarfs. In contrast, highly ionized O VI is commonly observed in sightlines that pass within the virial radius of a dwarf, and O VI detection rates are non-negligible at projected distances of 1$-$2$\times$ the virial radius. Based on these measurements, we estimate that the O VI-bearing phase of the CGM/IGM accounts for a dominant share of the metal budget of dwarf galaxies. The absorption kinematics suggest that a relatively modest fraction of the O VI-bearing gas is formally unbound. Together, these results imply that low-mass systems at $z\lesssim 1$ effectively retain a substantial fraction of their metals within the nearby CGM and IGM., Comment: Submitted to ApJ. 18 pages, 6 figures, 2 tables

The advent of quantum computers has justified the development of quantum machine learning algorithms , based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved in quantum computing, introducing the formal concept of quantum speed up. The survey provides a structured map of anomaly detection based on quantum machine learning. We have grouped existing algorithms according to the different learning methods, namely quantum supervised, quantum unsupervised and quantum reinforcement learning, respectively. We provide an estimate of the hardware resources to provide sufficient computational power in the future. The survey provides a systematic and compact understanding of the techniques belonging to each category. We eventually provide a discussion on the computational complexity of the learning methods in real application domains.

This paper studies emergent phenomena in neural networks by focusing on grokking where models suddenly generalize after delayed memorization. To understand this phase transition, we utilize higher-order mutual information to analyze the collective behavior (synergy) and shared properties (redundancy) between neurons during training. We identify distinct phases before grokking allowing us to anticipate when it occurs. We attribute grokking to an emergent phase transition caused by the synergistic interactions between neurons as a whole. We show that weight decay and weight initialization can enhance the emergent phase., Comment: ICML 2024 MI workshop

We classify up to isomorphism the gradings by arbitrary groups on the exceptional classical simple Lie superalgebras $G(3)$, $F(4)$ and $D(2,1;\alpha)$ over an algebraically closed field of characteristic $0$. To achieve this, we apply the recent method developed by A. Elduque and M. Kochetov to the known classification of fine gradings up to equivalence on the same superalgebras, which was obtained by C. Draper et al. in 2011. We also classify gradings on the simple Lie superalgebra $A(1,1)$, whose automorphism group is different from the other members of the $A$ series., Comment: 37 pages

We construct a model of ZFC with a singular cardinal $\kappa$ such that every subset of $\kappa$ in $L(V_{\kappa+1})$ has both the $\kappa$-Perfect Set Property and the $\mathcal{\vec{U}}$-Baire Property. This is a higher analogue of Solovay's result for $L(\mathbb{R})$. We obtain this configuration starting with large-cardinal assumptions in the realm of supercompactness, thus improving former theorems by Cramer, Shi and Woodin.

AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical and security risks due to their potential misuse. The rise of such advanced media has led to the development of a cognitive bias known as Impostor Bias, where individuals doubt the authenticity of multimedia due to the awareness of AI's capabilities. As a result, Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques, especially Convolutional Neural Networks (CNNs). Research in forensic Deepfake technology encompasses five main areas: detection, attribution and recognition, passive authentication, detection in realistic scenarios, and active authentication. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.

A time-efficient and comprehensive verification is a fundamental part of the design process for modern computing platforms, and it becomes ever more important and critical to optimize as the latter get ever more complex. SupeRFIVe is a methodology for the functional verification of superscalar processors that leverages an instruction set simulator to validate their correctness according to a simulation-based approach, interfacing a testbench for the design under test with the instruction set simulator by means of socket communication. We demonstrate the effectiveness of the SupeRFIVe methodology by applying it to verify the functional correctness of a RISC-V dual-issue superscalar CPU, leveraging the state-of-the-art RISC-V instruction set simulator Spike and executing a set of benchmark applications from the open literature., Comment: Accepted for lecture presentation at 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS), Nancy, France, Nov. 18-20, 2024

In this work, we examine the dynamical aspects of the cosmological Mixmaster model within the framework of non-commutative generalized uncertainty principle (GUP) theories. The theory is formulated classically by introducing a well-defined symplectic form that differs from the ordinary one, thereby inducing a general deformation of the Poisson brackets describing a precise class of GUP theories. In this general setting, we first investigate the behavior of the Bianchi I and Bianchi II models using Misner variables. Then, we study the Bianchi IX model in the Mixmaster approximation, which is well-known for accurately reproducing the dynamics of the point-particle Universe approaching the cosmological singularity. We derive the corresponding Belinsky-Khalatnikov-Lifshitz (BKL) map and then, by selecting a specific GUP model associated with string theory, we explicitly investigate its resulting features shaped by the non-commutative GUP scheme. Our findings reveal that the chaotic and ergodic behavior typically observed in the standard BKL map, which characterizes the point-Universe's approach to the singularity, is replaced by quasi-periodic orbits in the parameter space of the theory. This corresponds to an oscillatory behavior of the Universe's scale factors, dependent on the initial conditions., Comment: 23 pages, 3 figures

In this work, we show that it is possible to define a classical system associated with a Generalized Uncertainty Principle (GUP) theory via the implementation of a consistent symplectic structure. This provides a solid framework for the classical Hamiltonian formulation of such theories and the study of the dynamics of physical systems in the corresponding deformed phase space. By further characterizing the functions that govern non-commutativity in the configuration space using the algebra of angular momentum, we determine a general form for the rotation generator in these theories and crucially, we show that, under these conditions, unlike what has been previously found in the literature at the quantum level, this requirement does not lead to the superselection of GUP models at the classical level. Finally, we demonstrate that a properly defined GUP theory can be correctly interpreted classically if and only if the corresponding quantum commutators satisfy the Jacobi identities., Comment: 30 pages

In the pursuit of understanding the population of stellar remnants within the Milky Way, we analyze the sample of $\sim 950$ microlensing events observed by the Spitzer Space Telescope between 2014 and 2019. In this study we focus on a sub-sample of nine microlensing events, selected based on their long timescales, small microlensing parallaxes and joint observations by the Gaia mission, to increase the probability that the chosen lenses are massive and the mass is measurable. Among the selected events we identify lensing black holes and neutron star candidates, with potential confirmation through forthcoming release of the Gaia time-series astrometry in 2026. Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of $2.98^{+1.75}_{-1.28}~M_{\odot}$, $4.65^{+3.12}_{-2.08}~M_{\odot}$, $3.15^{+0.66}_{-0.64}~M_{\odot}$ and $1.4^{+0.75}_{-0.55}~M_{\odot}$, respectively. Notably, the first two candidates are expected to exhibit astrometric microlensing signals detectable by Gaia, offering the prospect of validating the lens masses. The methodologies developed in this work will be applied to the full Spitzer microlensing sample, populating and analyzing the time-scale ($t_{\rm E}$) vs. parallax ($\pi_{\rm E}$) diagram to derive constraints on the population of lenses in general and massive remnants in particular., Comment: submitted to ApJ

We present a flavour non-universal extension of the Standard Model combined with the idea of Higgs compositeness. At the TeV scale, the gauge groups $SU(2)_R$ and $U(1)_{B-L}$ are assumed to act in a non-universal manner on light- and third-generation fermions, while the Higgs emerges as a pseudo Nambu-Goldstone boson of the spontaneous global symmetry breaking $Sp(4)\to SU(2)_L\times SU(2)_R^{[3]}$, attributed to new strong dynamics. The flavour deconstruction means the couplings of the light families to the composite sector (and therefore the pNGB Higgs) are suppressed by powers of a heavy mass scale (from which the Higgs is nevertheless shielded by compositeness), explaining the flavour puzzle. We present a detailed analysis of the radiatively generated Higgs potential, showing how this intrinsically-flavoured framework has the ingredients to justify the unavoidable tuning in the Higgs potential necessary to separate electroweak and composite scales. This happens for large enough values of the $SU(2)_R^{[3]}$ gauge coupling and light enough flavoured gauge bosons resulting from the deconstruction, whose phenomenology is also investigated. The model is compatible with current experimental bounds and predicts new states at the TeV scale, which are within the reach of near future experimental searches., Comment: 36 pages, 4 figures, 1 table

A systematic study, based on the third-moment structure function, of Sgr A*'s variability finds an exponential rise time $\tau_{1,\rm{obs}}=14.8^{+0.4}_{-1.5}~\mathrm{minutes}$ and decay time $\tau_{2,\rm{obs}}=13.1^{+1.3}_{-1.4}~\mathrm{minutes}$. This symmetry of the flux-density variability is consistent with earlier work, and we interpret it as caused by the dominance of Doppler boosting, as opposed to gravitational lensing, in Sgr~A*'s light curve. A relativistic, semi-physical model of Sgr~A* confirms an inclination angle $i<45$ degrees. The model also shows that the emission of the intrinsic radiative process can have some asymmetry even though the observed emission does not. The third-moment structure function, which is a measure of the skewness of the light-curve increments, may be a useful summary statistic in other contexts of astronomy because it senses only temporal asymmetry, i.e., it averages to zero for any temporally symmetric signal., Comment: Accepted for publication in A&A letters

Effective-one-body (EOB) models are based on analytical building blocks that, mathematically, are truncated Taylor series with logarithms. These polynomials are usually resummed using Pad\'e approximants obtained first assuming that the logarithms are constant, and then replacing them back into the resulting rational functions. A recent study pointed out that this procedure introduces spurious logarithmic terms when the resummed functions are reexpanded. We therefore explore analytically more consistent resummation schemes. Here we update the TEOBResumS-Dali waveform model for spin-aligned, noncircularized, coalescing black hole binaries by systematically implementing new (still Pad\'e based) resummations for all EOB functions. Once the model is informed by 51 Numerical Relativity simulations this new approach proves key in lowering the maximum EOB/NR unfaithfulness $\bar{F}_{\rm EOBNR}^{\rm max}$ for the $\ell=m=2$ mode (with the Advanced LIGO noise in the total mass range $10-200M_{\odot}$) over the whole Simulating eXtreme Spacetimes catalog of 534 waveforms. A median mismatch of $3.418\times 10^{-4}$ is achieved, which is a marked improvement on the previous value, $1.06\times 10^{-3}$. The largest value, $\bar{\cal F}^{\rm max}_{\rm EOBNR}\sim 10^{-2}$, is found for an equal-mass, equal-spin simulation with dimensionless spins $\sim +0.994$; only six configurations (all equal-mass, and with equal spins larger than $+0.97$) are above $5\times 10^{-3}$. Results for eccentric binaries are similarly excellent (well below $10^{-2}$ and mostly around $10^{-3}$). For scattering configurations, we find an unprecedented EOB/NR agreement ($<1\%$) for relatively small values of the scattering angle, though differences progressively increase as the threshold of immediate capture is approached., Comment: 14 pages, 9 figures. Submitted to Phys. Rev. D

Dielectrics featuring a high relative permittivity, i.e., high-k dielectrics, have become the standard insulators in gate architectures, enhancing the electrical performance of both room temperature and cryogenic electronics. This study delves into the cryogenic (3 K) performance of high-k dielectrics commonly used as gate insulators. We fabricated Al2O3 and HfO2 layers via Atomic Layer Deposition (ALD) and we extrapolated relative permittivity (k) and dielectric strength (EBD) from AC (100 Hz to 100 kHz) and DC measurements on metal-insulator-metal capacitors. Our findings reveal a strong dependence of HfO2 cryogenic performance on the ALD growth temperature, while the latter shows a negligible impact on Al2O3. We estimated a ~9 % and ~14 % reduction of the relative permittivity of HfO2 and Al2O3, respectively, from 300 K to 3 K. Additionally, we designed and fabricated Al2O3/HfO2 bilayers and we checked their properties at cryogenic temperatures. The study also investigates the impact of the patterning method, namely, UV or electron-beam lithography (acceleration voltage of 10, 20, or 30 kV), on the high-k dielectric properties., Comment: 22 pages, 4 figures, supporting information at the end of the paper

Submillimeter and millimeter wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fields, but there are still many open questions that cannot be answered with current facilities. In this report we summarise the science that is guiding the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST). We demonstrate how tranformational advances in topics including star formation in high redshift galaxies, the diffuse circumgalactic medium, Galactic ecology, cometary compositions and solar flares motivate the need for a 50m, single-dish telescope with a 1-2 degree field of view and a new generation of highly multiplexed continuum and spectral cameras. AtLAST will have the resolution to drastically lower the confusion limit compared to current single-dish facilities, whilst also being able to rapidly map large areas of the sky and detect extended, diffuse structures. Its high sensitivity and large field of view will open up the field of submillimeter transient science by increasing the probability of serendipitous detections. Finally, the science cases listed here motivate the need for a highly flexible operations model capable of short observations of individual targets, large surveys, monitoring programmes, target of opportunity observations and coordinated observations with other observatories. AtLAST aims to be a sustainable, upgradeable, multipurpose facility that will deliver orders of magnitude increases in sensitivity and mapping speeds over current and planned submillimeter observatories., Comment: 47 pages, 12 figures. For further details on AtLAST see https://atlast.uio.no

Recent work has suggested using Monte Carlo methods based on piecewise deterministic Markov processes (PDMPs) to sample from target distributions of interest. PDMPs are non-reversible continuous-time processes endowed with momentum, and hence can mix better than standard reversible MCMC samplers. Furthermore, they can incorporate exact sub-sampling schemes which only require access to a single (randomly selected) data point at each iteration, yet without introducing bias to the algorithm's stationary distribution. However, the range of models for which PDMPs can be used, particularly with sub-sampling, is limited. We propose approximate simulation of PDMPs with sub-sampling for scalable sampling from posterior distributions. The approximation takes the form of an Euler approximation to the true PDMP dynamics, and involves using an estimate of the gradient of the log-posterior based on a data sub-sample. We thus call this class of algorithms stochastic-gradient PDMPs. Importantly, the trajectories of stochastic-gradient PDMPs are continuous and can leverage recent ideas for sampling from measures with continuous and atomic components. We show these methods are easy to implement, present results on their approximation error and demonstrate numerically that this class of algorithms has similar efficiency to, but is more robust than, stochastic gradient Langevin dynamics.

Manipulating underwater pressure waves is crucial for marine exploration, as electromagnetic signals are strongly absorbed in water. However, the multi-path phenomenon complicates the accurate capture of acoustic waves by receivers. Although graded index lenses, based on metamaterials with smoothly varying properties, successfully focus pressure waves, they tend to have high directivity, which hinders practical application. This work introduces three 2D acoustic lenses made from a metamaterial composed of solid inclusions in water. We propose an optimization scheme where the pressure dynamics is governed by Helmholtz's equation, with control parameters affecting each lens cell's density and bulk modulus. Through an appropriate cost function, the optimization encourages a broadband, low-directivity lens. The large-scale optimization is solved using the Lagrangian approach, which provides an analytical expression for the cost gradient. This scheme avoids the need for a separate discretization step, allowing the design to transition directly from the desired smooth refractive index to a practical lattice structure. As a result, the optimized lens closely aligns with real-world behavior. The homogenized numerical model is validated against finite elements, which considers acoustic-elastic coupling at the microstructure level. When homogenization holds, this approach proves to be an effective design tool for achieving broadband, low-directivity acoustic lenses., Comment: 18 pages, 7 figures

Filaments connecting halos are a long-standing prediction of cold dark matter theories. We present a novel detection of the cosmic web emission connecting two massive quasar-host galaxies at cosmic noon in the MUSE Ultra Deep Field (MUDF) using unprecedentedly deep observations that unlock a high-definition view of the filament morphology, a measure of the transition radius between the intergalactic and circumgalactic medium, and the characterization of the surface brightness profiles along the filament and in the transverse direction. Through systematic comparisons with simulations, we validate the filaments' typical density predicted in the current cold dark matter model. Our analysis of the MUDF field, an excellent laboratory for quantitatively studying filaments in emission, opens a new avenue to understanding the cosmic web that, being a fundamental prediction of cosmology, bears key information on the essence of dark matter., Comment: submitted, comments welcome!

We are proposing a hybrid superconductor-semiconductor platform using indium arsenide (InAs) grown on an insulating layer of indium aluminum arsenide (InAlAs) heterostructure (InAsOI) as an ideal candidate for coherent caloritronic devices. These devices aim to heat or cool electrons out of equilibrium with respect to the phonon degree of freedom. However, their performances are usually limited by the strength of the electron-phonon (e-ph) coupling and the associated power loss. Our work discusses the advantages of the InAsOI platform, which are based on the significantly low e-ph coupling measured compared to all-metallic state-of-the-art caloritronic devices. Our structure demonstrates values of the e-ph coupling constant up to two orders of magnitude smaller than typical values in metallic structures., Comment: 7 pages, 4 figures

Despite significant advances in the field of phononic crystals, the development of acoustic metafluids that replicate the behaviour of liquids in three dimensions remains elusive. For instance, water - the quintessential pentamode (PM) material - has a bulk modulus two orders of magnitude higher than current state-of-the-art PMs. The need for a low shear modulus inherently conflicts with the desire of high bulk modulus and density. In this letter, we shed light on the limitations of existing PM geometries and propose an innovative shape for the links that constitute the network. Inspired by the kinematics of ropes, these links are constructed from thin fibres and demonstrate the potential to create PMs with properties akin to those of liquids. As a prime example, we present the design of the first metamaterial that fully deserves the name 3D metal water, since its acoustic properties in the low frequency regime are indistinguishable with water. Additionally, we highlight a shear band gap in the lattice dispersion diagram, and illustrate the influence of geometric parameters on the dynamic properties at higher frequencies. This novel design of metafluids holds promise for applications requiring anisotropic materials such as acoustic lenses, waveguides, and cloaks., Comment: 13 pages, 6 figures

Generating realistic images to accurately predict changes in the structure of brain MRI is a crucial tool for clinicians. Such applications help assess patients' outcomes and analyze how diseases progress at the individual level. However, existing methods for this task present some limitations. Some approaches attempt to model the distribution of MRI scans directly by conditioning the model on patients' ages, but they fail to explicitly capture the relationship between structural changes in the brain and time intervals, especially on age-unbalanced datasets. Other approaches simply rely on interpolation between scans, which limits their clinical application as they do not predict future MRIs. To address these challenges, we propose a Temporally-Aware Diffusion Model (TADM), which introduces a novel approach to accurately infer progression in brain MRIs. TADM learns the distribution of structural changes in terms of intensity differences between scans and combines the prediction of these changes with the initial baseline scans to generate future MRIs. Furthermore, during training, we propose to leverage a pre-trained Brain-Age Estimator (BAE) to refine the model's training process, enhancing its ability to produce accurate MRIs that match the expected age gap between baseline and generated scans. Our assessment, conducted on the OASIS-3 dataset, uses similarity metrics and region sizes computed by comparing predicted and real follow-up scans on 3 relevant brain regions. TADM achieves large improvements over existing approaches, with an average decrease of 24% in region size error and an improvement of 4% in similarity metrics. These evaluations demonstrate the improvement of our model in mimicking temporal brain neurodegenerative progression compared to existing methods. Our approach will benefit applications, such as predicting patient outcomes or improving treatments for patients.

Implicit functions such as Neural Radiance Fields (NeRFs), occupancy networks, and signed distance functions (SDFs) have become pivotal in computer vision for reconstructing detailed object shapes from sparse views. Achieving optimal performance with these models can be challenging due to the extreme sparsity of inputs and distribution shifts induced by data corruptions. To this end, large, noise-free synthetic datasets can serve as shape priors to help models fill in gaps, but the resulting reconstructions must be approached with caution. Uncertainty estimation is crucial for assessing the quality of these reconstructions, particularly in identifying areas where the model is uncertain about the parts it has inferred from the prior. In this paper, we introduce Dropsembles, a novel method for uncertainty estimation in tuned implicit functions. We demonstrate the efficacy of our approach through a series of experiments, starting with toy examples and progressing to a real-world scenario. Specifically, we train a Convolutional Occupancy Network on synthetic anatomical data and test it on low-resolution MRI segmentations of the lumbar spine. Our results show that Dropsembles achieve the accuracy and calibration levels of deep ensembles but with significantly less computational cost.

We present a novel dataset that extends our view of the cosmic gas around z$\approx$3-4 Ly$\alpha$ emitting galaxies (LAEs) in the Muse Analysis of Gas around Galaxies (MAGG) survey by tracing a cool and enriched gas phase through 47 MgII absorbers identified in newly-obtained VLT/XSHOOTER near-infrared quasar spectra. Jointly with the more ionized gas traced by CIV systems and the neutral HI from previous work, we find that LAEs are distributed inside cosmic structures that contain multiphase gas in composition and temperature. All gas phases are a strong function of the large-scale galaxy environment: the MgII and the CIV strength and kinematics positively correlate with the number of associated galaxies, and it is $\approx$3-4 times more likely to detect metal absorbers around group than isolated LAEs. Exploring the redshift evolution, the covering factor of MgII around group and isolated LAEs remains approximately constant from z$\approx$3-4 to z<2, but the one of CIV around group galaxies drops by z<2. Adding the cool enriched gas traced by the MgII absorbers to the results we obtained for the HI and CIV gas, we put forward a picture in which LAEs lie along gas filaments that contain high column-density HI systems and are enriched by strong CIV and MgII absorbers. While the MgII gas appears to be more centrally concentrated near LAEs, weaker CIV systems trace instead a more diffuse gas phase extended up to larger distances around the galaxies., Comment: Accepted for publication on A&A

Given the compact binary evolution problem of numerical relativity, in the finite-difference, block-based, adaptive mesh refinement context, choices must be made on how evolved fields are to be discretized. In GR-Athena++, the space-time solver was previously fixed to be vertex-centered. Here, our recent extensions to a cell-centered treatment, are described. Simplifications in the handling of variables during the treatment of general relativistic magneto-hydrodynamical (GRMHD) evolution are found. A novelty is that performance comparison for the two choices of grid sampling is made within a single code-base. In the case of a binary black hole inspiral-merger problem, by evolving geometric fields on vertex-centers, an average $\sim 20\%$ speed increase is observed, when compared against cell-centered sampling. The opposite occurs in the GRMHD setting. A binary neutron star inspiral-merger-collapse problem, representative of typical production simulations is considered. We find that cell-centered sampling for the space-time solver improves performance, by a similar factor.

Exploring the shape of point configurations has been a key driver in the evolution of TDA (short for topological data analysis) since its infancy. This survey illustrates the recent efforts to broaden these ideas to model spatial interactions among multiple configurations, each distinguished by a color. It describes advances in this area and prepares the ground for further exploration by mentioning unresolved questions and promising research avenues while focusing on the overlap with discrete geometry.

Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://github.com/LemuelPuglisi/SynthBA.

We study initially unbound systems of two black holes using numerical relativity (NR) simulations performed with GR-Athena++. We focus on regions of the parameter space close to the transition from scatterings to dynamical captures, considering equal mass and spin-aligned configurations, as well as unequal mass and nonspinning ones. The numerical results are then used to validate the effective-one-body (EOB) model TEOBResumS-Dal\'i for dynamical captures and scatterings. We find good agreement for the waveform phenomenologies, scattering angles, mismatches, and energetics in the low energy regime ($E_0\lesssim 1.02\,M$). In particular, mismatches are typically below or around the $1\%$ level, with only a few cases -- corresponding to spinning binaries -- slightly above the $3\%$ threshold. We also discuss dynamical captures in the test-mass limit by solving numerically the Zerilli equation with the time domain code RWZHyp. The latter analysis provides valuable insights into both the analytical noncircular corrections of the EOB waveform and the integration of NR Weyl scalars., Comment: 31 pages, 18 figures

In numerous fields, dynamic time series data require continuous updates, necessitating efficient data processing techniques for accurate analysis. This paper examines the banana tree data structure, specifically designed to efficiently maintain persistent homology -- a multi-scale topological descriptor -- for dynamically changing time series data. We implement this data structure and conduct an experimental study to assess its properties and runtime for update operations. Our findings indicate that banana trees are highly effective with unbiased random data, outperforming state-of-the-art static algorithms in these scenarios. Additionally, our results show that real-world time series share structural properties with unbiased random walks, suggesting potential practical utility for our implementation.

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

This paper presents an extensive characterization of the low-frequency noise (LFN) at room temperature (RT) and cryogenic temperature (4.2 K) of 40-nm bulk-CMOS transistors. The noise is measured over a wide range of bias conditions and geometries to generate a comprehensive overview of LFN in this technology. While the RT results are in-line with the literature and the foundry models, the cryogenic behavior diverges in many aspects. These deviations include changes with respect to RT in magnitude and bias dependence that are conditional on transistor type and geometry, and even an additional systematic Lorentzian feature that is common among individual devices. Furthermore, we find the scaling of the average LFN with the area and its variability to be similar between RT and 4.2 K, with the cryogenic scaling reported systematically for the first time. The findings suggest that, as no consistent decrease of LFN at lower temperatures is observed while the white noise is reduced, the impact of LFN for precision analog design at cryogenic temperatures gains a more predominant role.

Circumgalactic Lyman-alpha (Ly$\alpha$) nebulae are gaseous halos around galaxies exhibiting luminous extended Ly$\alpha$ emission. This work investigates Ly$\alpha$ nebulae from deep imaging of $\sim12~\mathrm{deg}^2$ sky, targeted by the MAMMOTH-Subaru survey. Utilizing the wide-field capability of Hyper Suprime-Cam (HSC), we present one of the largest blind Ly$\alpha$ nebula selections, including QSO nebulae, Ly$\alpha$ blobs, and radio galaxy nebulae down to typical $2\sigma$ Ly$\alpha$ surface brightness of $(5-10)\times10^{-18}\mathrm{~erg~s^{-1}~cm^{-2}~arcsec^{-2}}$. The sample contains 117 nebulae with Ly$\alpha$ sizes of 40 - 400 kpc, and the most gigantic one spans about 365 kpc, referred to as the Ivory Nebula. Combining multiwavelength data, we investigate diverse nebula populations and associated galaxies. We find a small fraction of Ly$\alpha$ nebulae have QSOs ($\sim7\%$), luminous infrared galaxies ($\sim1\%$), and radio galaxies ($\sim 2\%$). Remarkably, among the 28 enormous Ly$\alpha$ nebulae (ELANe) exceeding 100 kpc, about 80\% are associated with UV-faint galaxies ($M_\mathrm{UV} > -22$), categorized as Type II ELANe. We underscore that Type II ELANe constitute the majority but remain largely hidden in current galaxy and QSO surveys. Dusty starburst and obscured AGN activity are proposed to explain the nature of Type II ELANe. The SED of stacking all Ly$\alpha$ nebulae also reveals signs of massive dusty star-forming galaxies with obscured AGNs. We propose a model to explain the dusty nature where the diverse populations of Ly$\alpha$ nebulae capture massive galaxies at different evolutionary stages undergoing violent assembling. Ly$\alpha$ nebulae provide critical insights into the formation and evolution of today's massive cluster galaxies at cosmic noon., Comment: Accepted by ApJS after minor revision; 26 pages, 10 figures, 3 tables