We report observations of the ultra-high-energy gamma-ray source LHAASO J2108$+$5157, utilizing VERITAS, HAWC, Fermi-LAT, and XMM-Newton. VERITAS has collected $\sim$ 40 hours of data that we used to set ULs to the emission above 200 GeV. The HAWC data, collected over $\sim 2400$ days, reveal emission between 3 and 146 TeV, with a significance of $7.5~\sigma$, favoring an extended source model. The best-fit spectrum measured by HAWC is characterized by a simple power-law with a spectral index of $2.45\pm0.11_{stat}$. Fermi-LAT analysis finds a point source with a very soft spectrum in the LHAASO J2108+5157 region, consistent with the 4FGL-DR3 catalog results. The XMM-Newton analysis yields a null detection of the source in the 2 - 7 keV band. The broadband spectrum can be interpreted as a pulsar and a pulsar wind nebula system, where the GeV gamma-ray emission originates from an unidentified pulsar, and the X-ray and TeV emission is attributed to synchrotron radiation and inverse Compton scattering of electrons accelerated within a pulsar wind nebula. In this leptonic scenario, our X-ray upper limit provides a stringent constraint on the magnetic field, which is $\lesssim 1.5\ \mu$G.

Galaxy clusters are expected to be dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster's formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at gamma-ray energies and are predicted to be sources of large-scale gamma-ray emission due to hadronic interactions in the intracluster medium. We estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. We perform a detailed spatial and spectral modelling of the expected signal for the DM and the CRp components. For each, we compute the expected CTA sensitivity. The observing strategy of Perseus is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio within the radius $R_{500}$ down to about $X_{500}<3\times 10^{-3}$, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index $\alpha_{\rm CRp}=2.3$. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure $\alpha_{\rm CRp}$ down to about $\Delta\alpha_{\rm CRp}\simeq 0.1$ and the CRp spatial distribution with 10% precision. Regarding DM, CTA should improve the current ground-based gamma-ray DM limits from clusters observations on the velocity-averaged annihilation cross-section by a factor of up to $\sim 5$, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with \tau_{\chi}&gt;10^{27}s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario.

Restoring underwater images affected by non-uniform illumination (NUI) is essential to improve visual quality and usability in marine applications. Conventional methods often fall short in handling complex illumination patterns, while learning-based approaches face challenges due to the lack of targeted datasets. To address these limitations, the Underwater Non-uniform Illumination Restoration Network (UNIR-Net) is proposed. UNIR-Net integrates multiple components, including illumination enhancement, attention mechanisms, visual refinement, and contrast correction, to effectively restore underwater images affected by NUI. In addition, the Paired Underwater Non-uniform Illumination (PUNI) dataset is introduced, specifically designed for training and evaluating models under NUI conditions. Experimental results on PUNI and the large-scale real-world Non-Uniform Illumination Dataset (NUID) show that UNIR-Net achieves superior performance in both quantitative metrics and visual outcomes. UNIR-Net also improves downstream tasks such as underwater semantic segmentation, highlighting its practical relevance. The code of this method is available at this https URL

Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With over a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich dataset, we introduce a machine learning approach for gamma-hadron separation. A Multilayer Perceptron shows the best performance, surpassing traditional and other Machine Learning based methods. This approach shows a notable improvement in the detector's sensitivity, supported by results from both simulated and real HAWC data. In particular, it achieves a 19\% increase in significance for the Crab Nebula, commonly used as a benchmark. These improvements highlight the potential of machine learning to significantly enhance the performance of HAWC and provide a valuable reference for ground-based observatories, such as Large High Altitude Air Shower Observatory (LHAASO) and the upcoming Southern Wide-field Gamma-ray Observatory (SWGO).

This paper presents the design of an artificial vision system prototype for automatic inspection and monitoring of objects over a conveyor belt and using a Smart camera 2D BOA-INS. The prototype consists of a conveyor belt and an embedded system based on an Arduino Mega card for system control, and it has as main peripherals the smart camera, a direct current motor, a photoelectric sensor, LED illumination and LEDs indicating the status (good or defect) of each evaluated object. The application of the prototype is for educational purposes, so that undergraduate, master and diploma students can simulate a continuous production line, controlled by an embedded system, and perform quality control by monitoring through a visual system and a personal computer. This allows implementing the topics of embedded systems, artificial vision, artificial intelligence, pattern recognition, automatic control, as well as automation of real processes.

The Choi representation of completely positive (CP) maps, i.e. quantum channels is often used in the context of quantum information and computation as it is easy to work with. It is a correspondence between CP maps and quantum states also termed as the Choi-Jamio\l kowski isomorphism. It is especially useful if a parametrization of the set of CP maps is needed in order to consider a general map or optimize over the set of these. Here we provide a brief introduction to this topic, focusing on certain useful calculational techniques which are presented in full detail.

Cosmic rays with energies up to a few PeV are known to be accelerated within the Milky Way. Traditionally, it has been presumed that supernova remnants were the main source of very-high-energy cosmic rays but theoretically it is difficult to get protons to PeV energies and observationally there simply is no evidence to support the remnants as sources of hadrons with energies above a few tens of TeV. One possible source of protons with those energies is the Galactic Center region. Here we report observations of 1-100 TeV gamma rays coming from the 'Cygnus Cocoon', which is a superbubble surrounding a region of OB2 massive star formation. These gamma rays are likely produced by 10-1000 TeV freshly accelerated CRs originating from the enclosed star forming region Cygnus OB2. Hitherto it was not known that such regions could accelerate particles to these energies. The measured flux is likely originated by hadronic interactions. The spectral shape and the emission profile of the Cocoon changes from GeV to TeV energies, which reveals the transport of cosmic particles and historical activity in the superbubble.

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-Königl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

Very-high-energy (0.1-100 TeV) gamma-ray emission was observed in HAWC data from the lobes of the microquasar SS 433, making them the first set of astrophysical jets that were resolved at TeV energies. In this work, we update the analysis of SS 433 using 2,565 days of data from the High Altitude Water Cherenkov (HAWC) observatory. Our analysis reports the detection of a point-like source in the east lobe at a significance of $6.6\,\sigma$ and in the west lobe at a significance of $8.2\,\sigma$. For each jet lobe, we localize the gamma-ray emission and identify a best-fit position. The locations are close to the X-ray emission sites "e1" and "w1" for the east and west lobes, respectively. We analyze the spectral energy distributions and find that the energy spectra of the lobes are consistent with a simple power-law $\text{d}N/\text{d}E\propto E^{\alpha}$ with $\alpha = -2.44^{+0.13+0.04}_{-0.12-0.04}$ and $\alpha = -2.35^{+0.12+0.03}_{-0.11-0.03}$ for the east and west lobes, respectively. The maximum energy of photons from the east and west lobes reaches 56 TeV and 123 TeV, respectively. We compare our observations to various models and conclude that the very-high-energy gamma-ray emission can be produced by a population of electrons that were efficiently accelerated.

We investigate the origin of the elliptical ring structure observed in the images of the supermassive black hole M87*, aiming to disentangle contributions from gravitational, astrophysical, and imaging effects. Leveraging the enhanced capabilities of the Event Horizon Telescope (EHT) 2018 array, including improved $(u,v)$-coverage from the Greenland Telescope, we measure the ring's ellipticity using five independent imaging methods, obtaining a consistent average value of $\tau = 0.08_{-0.02}^{+0.03}$ with a position angle $\xi = 50.1_{-7.6}^{+6.2}$ degrees. To interpret this measurement, we compare against General Relativistic Magnetohydrodynamic (GRMHD) simulations spanning a wide range of physical parameters including thermal or non-thermal electron distribution function, spins, and ion-to-electron temperature ratios in both low and high-density regions. We find no statistically significant correlation between spin and ellipticity in GRMHD images. Instead, we identify a correlation between ellipticity and the fraction of non-ring emission, particularly in non-thermal models and models with higher jet emission. These results indicate that the ellipticity measured from the \m87 emission structure is consistent with that expected from simulations of turbulent accretion flows around black holes, where it is dominated by astrophysical effects rather than gravitational ones. Future high-resolution imaging, including space very long baseline interferometry and long-term monitoring, will be essential to isolate gravitational signatures from astrophysical effects.

The dSphs around the Milky Way are commonly considered as systems that are supported by velocity dispersion against self-gravitation. They have been long accounted among the best targets to search for indirect DM signatures in the GeV-to-TeV gamma-rays due to absence of astrophysical gamma-ray foreground or background emission. We present forecasts on the sensitivity of the future CTAO for the search for annihilating or decaying DM in such targets. We perform an original selection of candidates out of the current catalog of known objects, including both classical and ultra-faint targets. For each of them, we calculate the expected amount of DM using the most updated and complete available samples of photometric and spectroscopic data of member stars, adopting a common framework of data treatment for both classes of objects. In this way, we are able to generate novel astrophysical factor profiles for general indirect DM searches that we compare with the current literature. Out of a starting sample of 64 dSphs, we highlight the 8 most promising targets - DraI, CBe, UMaII, UMi and Wil1 in the Northern hemisphere; RetII, Scl and SgrII in the Southern hemisphere - for which different DM density models (either cored or cuspy) lead to similar expectations, at variance with what happens for other DM targets - thus resulting in more robust predictions. We find that CTAO will provide the strongest limits above ~10 TeV, down to values of velocity-averaged annihilation cross section of ~5$\times 10^{-25}$ cm$^3$ s$^{-1}$ and up to decay lifetimes of ~10$^{26}$ s for combined limits on the best targets. We argue that the largest source of inaccuracy is due to the still imprecise determination of the DM content, especially for ultra-faint dSphs. We propose possible strategies of observation for CTAO, either optimized on a deep focus on the best known candidates, or on the diversification of targets.

Gravitational wave detectors such as KAGRA, a 3-km long underground laser interferometer in Japan, require elaborate passive and active seismic isolation of their mirrors. With the aim of detecting passing gravitational waves that create a relative mirror displacement of less than $10^{-22}\ \mathrm{m}$ at frequencies of tens to hundreds of Hz, all environmental couplings must be stringently suppressed. This paper presents the result of applying the H-infinity optimization method to the active seismic isolation of a gravitational-wave detector for the first time. The so-called \textit{sensor correction} and \textit{sensor fusion} schemes of the seismic attenuation system of KAGRA's signal recycling mirror are used as a test bed. We designed and implemented optimal sensor correction and sensor fusion filters, resulting in a sevenfold attenuation of seismic noise coupling to the signal recycling mirror in the 0.1-0.5 Hz band, with the downstream effect of an 88.2% noise performance improvement in the same frequency band. When combined with other hardware upgrades, the implementation of sensor correction and sensor fusion contributed to an increase in KAGRA's duty cycle from 53% in the O3GK observation run to 80% in O4a, demonstrating the effectiveness of the H-infinity optimization approach.

Low-light image enhancement is an important task in computer vision, essential for improving the visibility and quality of images captured in non-optimal lighting conditions. Inadequate illumination can lead to significant information loss and poor image quality, impacting various applications such as surveillance. photography, or even autonomous driving. In this regard, automated methods have been developed to automatically adjust illumination in the image for a better visual perception. Current enhancement techniques often use specific datasets to enhance low-light images, but still present challenges when adapting to diverse real-world conditions, where illumination degradation may be localized to specific regions. To address this challenge, the Adaptive Light Enhancement Network (ALEN) is introduced, whose main approach is the use of a classification mechanism to determine whether local or global illumination enhancement is required. Subsequently, estimator networks adjust illumination based on this classification and simultaneously enhance color fidelity. ALEN integrates the Light Classification Network (LCNet) for illuminance categorization, complemented by the Single-Channel Network (SCNet), and Multi-Channel Network (MCNet) for precise estimation of illumination and color, respectively. Extensive experiments on publicly available datasets for low-light conditions were carried out to underscore ALEN's robust generalization capabilities, demonstrating superior performance in both quantitative metrics and qualitative assessments when compared to recent state-of-the-art methods. The ALEN not only enhances image quality in terms of visual perception but also represents an advancement in high-level vision tasks, such as semantic segmentation, as presented in this work. The code of this method is available at this https URL

We propose a new type of the Unruh-DeWitt detector which measures the decoherence of the reduced density matrix of the detector interacting with the massless quantum scalar field. We find that the decoherence decay rates are different in the inertial and accelerated reference frames. We show that the exponential phase decay can be observed for relatively low accelerations, that can significantly improve the conditions for measuring the Unruh effect.

To determine the nature of the PeVatron's emission (hadronic or leptonic), it is essential to characterize the physical parameters of the environment from where it originates. We unambiguously confirm the association of molecular gas with the PeVatron candidate LHAASO J2108+5157 using unprecedented high angular-resolution (17$^{\prime \prime}$) $^{12,13}$CO($J$=1$\rightarrow$0) observations carried out with the Nobeyama 45m radio telescope. We characterize a molecular cloud in the vicinity of the PeVatron candidate LHAASO J2108+5157 by determining its physical parameters from our $^{12,13}$CO($J$=1$\rightarrow$0) line observations. We use an updated estimation of the distance to the cloud, which allows us to obtain a more reliable result. The molecular emission is compared with excess gamma-ray images obtained with Fermi--LAT at energies above 2 GeV to search for spatial correlations and test a possible hadronic ($\pi^0$ decay) origin for the gamma-ray emission. We find that the morphology of the spatial distribution of the CO emission is strikingly similar to that of the Fermi--LAT excess gamma-ray. By combining our observations with archival 21cm HI line data, the nucleons (HI + H$_2$) number density of the target molecular cloud is found to be 133.0 $\pm$ 45.0 cm$^{-3}$, for the measured angular size of 0.55 $\pm$ 0.02$^\circ$ at a distance of 1.6 $\pm$ 0.1 kpc. The resulting total mass of the cloud is M(HI +H$_2$) = 7.5$\pm$2.9$\times$10$^3$ M$_{\odot}$. Under a hadronic scenario, we obtain a total energy of protons of W$_p$ = 4.3$\pm$1.5 $\times$ 10$^{46}$ erg with a cutoff of 700$\pm$300 TeV, which reproduces the sub-PeV gamma-ray emission. We identified a molecular cloud in the vicinity of LHAASO J2107+5157 as the main target where cosmic rays from an unknown PeVatron produce the observed gamma-ray emission via $\pi^0$ decay.

We report the results of a simultaneous X-ray and optical spectroscopy campaign on the Galactic black hole X-ray binary V4641 Sgr, carried out with XRISM and the Seimei telescope during a low-luminosity phase towards the end of its 2024 outburst. Despite a very low X-ray luminosity of $10^{34}$ erg s$^{-1}$, the continuum spectrum is well reproduced by a disk blackbody model with a high inner disk temperature ($1.8$ keV). XRISM/Resolve provides the highest-resolution X-ray spectrum ever obtained from the source, and several strong, narrow emission lines were detected, resolved and characterized at a high significance level. The continuum shape and narrow emission lines both indicate that the inner disk region is obscured by the surrounding high-density gas, and the intrinsic luminosity is several orders of magnitude higher. In the simultaneous optical observation from the Seimei telescope, the line features are largely dominated by the optical companion. Although we detect a clear emission component in H$\alpha$ that could originate from a cold outflow or the disk atmosphere, there are no signs of the strong outflow signatures historically detected in this source. In X-rays, the combination of significantly redshifted ($\sim 700$ km s$^{-1}$) and weakly blueshifted ($\sim-250$ km s$^{-1}$) components, all varying strongly on ks timescales, along with a marginally significant (99.2%) highly blueshifted ($\sim-1200$ km s$^{-1}$) component, indicates a complex, inhomogeneous outflow geometry. This is corroborated by the erratic long-term evolution of the source seen in the complementary X-ray monitoring, and radio detections spanning 3 orders of magnitude.

Automatic circle detection in digital images has received considerable attention over the last years in computer vision as several efforts have aimed for an optimal circle detector. This paper presents an algorithm for automatic detection of circular shapes that considers the overall process as an optimization problem. The approach is based on the Harmony Search Algorithm (HSA), a derivative free meta-heuristic optimization algorithm inspired by musicians while improvising new harmonies. The algorithm uses the encoding of three points as candidate circles (harmonies) over the edge-only image. An objective function evaluates (harmony quality) if such candidate circles are actually present in the edge image. Guided by the values of this objective function, the set of encoded candidate circles are evolved using the HSA so that they can fit to the actual circles on the edge map of the image (optimal harmony). Experimental results from several tests on synthetic and natural images with a varying complexity range have been included to validate the efficiency of the proposed technique regarding accuracy, speed and robustness.

The Entropic Associative Memory holds objects in a 2D relation or ``memory plane'' using a finite table as the medium. Memory objects are stored by reinforcing simultaneously the cells used by the cue, implementing a form of Hebb's learning rule. Stored objects are ``overlapped'' on the medium, hence the memory is indeterminate and has an entropy value at each state. The retrieval operation constructs an object from the cue and such indeterminate content. In this paper we present the extension to the hetero-associative case in which these properties are preserved. Pairs of hetero-associated objects, possibly of different domain and/or modalities, are held in a 4D relation. The memory retrieval operation selects a largely indeterminate 2D memory plane that is specific to the input cue; however, there is no cue left to retrieve an object from such latter plane. We propose three incremental methods to address such missing cue problem, which we call random, sample and test, and search and test. The model is assessed with composite recollections consisting of manuscripts digits and letters selected from the MNIST and the EMNIST corpora, respectively, such that cue digits retrieve their associated letters and vice versa. We show the memory performance and illustrate the memory retrieval operation using all three methods. The system shows promise for storing, recognizing and retrieving very large sets of object with very limited computing resources.

Hough transform (HT) has been the most common method for circle detection exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches include heuristic methods that employ iterative optimization procedures for detecting multiple circles under the inconvenience that only one circle can be marked at each optimization cycle demanding a longer execution time. On the other hand, Learning Automata (LA) is a heuristic method to solve complex multi-modal optimization problems. Although LA converges to just one global minimum, the final probability distribution holds valuable information regarding other local minima which have emerged during the optimization process. The detection process is considered as a multi-modal optimization problem, allowing the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three edge points as parameters to determine circles candidates. A reinforcement signal determines if such circle candidates are actually present at the image. Guided by the values of such reinforcement signal, the set of encoded candidate circles are evolved using the LA so that they can fit into actual circular shapes over the edge-only map of the image. The overall approach is a fast multiple-circle detector despite facing complicated conditions.