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Python Testbed for Federated Learning Algorithms (PTB-FLA) is a simple FL framework targeting smart Internet of Things in edge systems that provides both generic centralized and decentralized FL algorithms, which implement the corresponding FL orchestration protocols that were formally verified using the process algebra CSP. This approach is appropriate for systems with stationary nodes but cannot be applied to systems with moving nodes. In this paper, we use celestial mechanics to model spacecraft movement, and timed automata (TA) to formalize and verify the centralized FL orchestration protocol, in two phases. In the first phase, we created a conventional TA model to prove traditional properties, namely deadlock freeness and termination. In the second phase, we created a stochastic TA model to prove timing correctness and to estimate termination probability., Comment: 4 pages, 5 figures, submitted to a conference

One of the many measures of the non-equilibrium nature of a system is the existence of a non-zero steady state current which is especially relevant for many biological systems. To this end, we study the non-equilibrium dynamics of a particle moving in a tilted colored noise ratchet in two different situations. In the first, the colored noise variable is reset to a specific value every time the particle transitions from one well to another in the ratchet. Contrary to intuition, we find that the current magnitude decreases as the correlation time of the noise increases, and increases monotonically with noise strength. The average displacement of the particle is against the tilt, which implies that the particle performs work. We then consider a variation of the same problem in which the colored noise process is allowed to evolve freely without any resetting at the transitions. Again, the average displacement is against the potential. However, the current magnitude increases with the correlation time, and there is an optimal noise strength that maximizes the current magnitude. Finally, we provide quantitative arguments to explain these findings and their relevance to active biological matter such as tissues.

In this paper, we investigate the counter-forensic effects of the forthcoming JPEG AI standard based on neural image compression, focusing on two critical areas: deepfake image detection and image splicing localization. Neural image compression leverages advanced neural network algorithms to achieve higher compression rates while maintaining image quality. However, it introduces artifacts that closely resemble those generated by image synthesis techniques and image splicing pipelines, complicating the work of researchers when discriminating pristine from manipulated content. We comprehensively analyze JPEG AI's counter-forensic effects through extensive experiments on several state-of-the-art detectors and datasets. Our results demonstrate that an increase in false alarms impairs the performance of leading forensic detectors when analyzing genuine content processed through JPEG AI. By exposing the vulnerabilities of the available forensic tools we aim to raise the urgent need for multimedia forensics researchers to include JPEG AI images in their experimental setups and develop robust forensic techniques to distinguish between neural compression artifacts and actual manipulations.

Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, there is currently a shortage of pixel-wise annotated HSI data necessary for training deep learning (DL) models. Additionally, the number of HSI-based research studies remains limited, and in many cases, the advantages of HSI over traditional RGB imaging have not been conclusively demonstrated, particularly for specimens collected intraoperatively. To address these challenges we present a database consisted of 27 HSIs of hematoxylin-eosin stained frozen sections, collected from 14 patients with colon adenocarcinoma metastasized to the liver. It is aimed to validate pixel-wise classification for intraoperative tumor resection. The HSIs were acquired in the spectral range of 450 to 800 nm, with a resolution of 1 nm, resulting in images of 1384x1035 pixels. Pixel-wise annotations were performed by three pathologists. To overcome challenges such as experimental variability and the lack of annotated data, we combined label-propagation-based semi-supervised learning (SSL) with spectral-spatial features extracted by: the multiscale principle of relevant information (MPRI) method and tensor singular spectrum analysis method. Using only 1% of labeled pixels per class the SSL-MPRI method achieved a micro balanced accuracy (BACC) of 0.9313 and a micro F1-score of 0.9235 on the HSI dataset. The performance on corresponding RGB images was lower, with a micro BACC of 0.8809 and a micro F1-score of 0.8688. These improvements are statistically significant. The SSL-MPRI approach outperformed six DL architectures trained with 63% of labeled pixels. Data and code are available at: https://github.com/ikopriva/ColonCancerHSI., Comment: 12 pages, 5 figures, 5 tables

The mechanism that controls the superconducting (SC) transition temperature $T_{\mathrm{c}}^{0}$ as a function of doping is one of the central questions in cuprate high-temperature superconductors. While it is generally accepted that $T_{\mathrm{c}}^{0}$ in underdoped cuprates is not determined by the scale of pairing but by the onset of global phase coherence, the role of phase fluctuations in the overdoped region has been controversial. Here, our transport measurements in perpendicular magnetic fields ($H$) on underdoped Bi-2201 reveal immeasurably small Hall response for $T>T_{\mathrm{c}}(H)$ as a signature of SC phase with vortexlike phase fluctuations. We find that the extent of such a regime in $T$ and $H$ is suppressed near optimal doping but becomes strongly enhanced in heavily overdoped Bi-2201. Our results thus show that vortexlike phase fluctuations play an important role in the field-tuned SC transition in the heavily overdoped region, in contrast to conventional mean-field Bardeen-Cooper-Schrieffer description. The unexpected nonmonotonic dependence of phase fluctuations on doping provides a new perspective on the SC transition in cuprates., Comment: 29 pages, incl. 4 figs + 10 suppl. figs

Silicon photonics is a leading platform for realizing the vast numbers of physical qubits needed for useful quantum information processing because it leverages mature complementary metal-oxide-semiconductor (CMOS) manufacturing to integrate on-chip thousands of optical devices for generating and manipulating quantum states of light. A challenge to the practical operation and scale-up of silicon quantum-photonic integrated circuits, however, is the need to control their extreme sensitivity to process and temperature variations, free-carrier and self-heating nonlinearities, and thermal crosstalk. To date these challenges have been partially addressed using bulky off-chip electronics, sacrificing many benefits of a chip-scale platform. Here, we demonstrate the first electronic-photonic quantum system-on-chip (EPQSoC) consisting of quantum-correlated photon-pair sources stabilized via on-chip feedback control circuits, all fabricated in a high-volume 45nm CMOS microelectronics foundry. We use non-invasive photocurrent sensing in a tunable microring cavity photon-pair source to actively lock it to a fixed pump laser while operating in the quantum regime, enabling large scale microring-based quantum systems. In this first demonstration of such a capability, we achieve a high CAR of 134 with an ultra-low g(2)(0) of 0.021 at 2.2 kHz off-chip detected pair rate and 3.3 MHz/mW2 on-chip pair generation efficiency, and over 100 kHz off-chip detected pair rate at higher pump powers (1.5 MHz on-chip). These sources maintain stable quantum properties in the presence of temperature variations, operating reliably in practical settings with many adjacent devices creating thermal disturbances on the same chip. Such dense electronic-photonic integration enables implementation and control of quantum-photonic systems at the scale required for useful quantum information processing with CMOS-fabricated chips.

Multivariate random effects with unstructured variance-covariance matrices of large dimensions, $q$, can be a major challenge to estimate. In this paper, we introduce a new implementation of a reduced-rank approach to fit large dimensional multivariate random effects by writing them as a linear combination of $d < q$ latent variables. By adding reduced-rank functionality to the package glmmTMB, we enhance the mixed models available to include random effects of dimensions that were previously not possible. We apply the reduced-rank random effect to two examples, estimating a generalized latent variable model for multivariate abundance data and a random-slopes model.

The BL Lac Object PKS 1717+177 has been identified as potential neutrino-emitting AGN in the point source stacking analysis of IceCube data. We explore peculiarities in the morphology and kinematics of the jet and examine multi-wavelength light curves for distinctive effects which might allow to pinpoint a likely neutrino generation mechanism. We re-modeled 34 high resolution radio interferometric Very Long Baseline Array (VLBA) observations obtained at 15 GHz (between 1999/12/27 and 2023/05/03). A correlation and periodicity analysis of optical KAIT and Tuorla data, as well as for Fermi-LAT $\gamma$-ray data has been performed. The nuclear jet appears deflected and bent at about 0.5 mas distance from the radio core by an encounter with a dark, unseen object. The deviation of the jet evolves over 23.5 years from a simple apparent bend into a significantly meandering structure with increasing amplitude: a zig-zag line. To our knowledge, this is the first time that the temporal evolution of a jet deviation can be traced. The turning point shifts with time and the jet seems to brighten up almost periodically at the point of deviation. The radio core as well as the jet contribute approximately equally to the total flux-density at 15 GHz. We discuss scenarios which could explain the complex jet bending and quasi-regular flaring. We propose that the jet could either be deflected by the magnetosphere of a second massive black hole, by the pressure gradient due to a circumnuclear dense cloud, or via gravitational lensing by an intervening black hole., Comment: 22 pages, 9 figures, in print in MNRAS

Robots are frequently tasked to gather relevant sensor data in unknown terrains. A key challenge for classical path planning algorithms used for autonomous information gathering is adaptively replanning paths online as the terrain is explored given limited onboard compute resources. Recently, learning-based approaches emerged that train planning policies offline and enable computationally efficient online replanning performing policy inference. These approaches are designed and trained for terrain monitoring missions assuming a single specific map representation, which limits their applicability to different terrains. To address these issues, we propose a novel formulation of the adaptive informative path planning problem unified across different map representations, enabling training and deploying planning policies in a larger variety of monitoring missions. Experimental results validate that our novel formulation easily integrates with classical non-learning-based planning approaches while maintaining their performance. Our trained planning policy performs similarly to state-of-the-art map-specifically trained policies. We validate our learned policy on unseen real-world terrain datasets., Comment: 8 pages, 4 figures

In recent years there has been significant debate around the impact of dust on SNe Ia, a major source of uncertainty in cosmological analyses. We perform the first cross-comparison of the probabilistic hierarchical SN Ia SED model BayeSN with the conventional SALT model, an important test given the history of conflicting conclusions regarding the distributions of host galaxy dust properties between the two. Applying BayeSN to SALT-based simulations, we find that BayeSN is able to accurately recover our simulated inputs, establishing excellent consistency between the two models. When inferring dust parameters with simulated samples including non-Ia contamination, we find that our choice of photometric classifier causes a bias in the inferred dust distribution; this arises because SNe Ia heavily impacted by dust are misclassified as contaminants and excluded. We then apply BayeSN to the sample of SNe from DES5YR to jointly infer host galaxy dust distributions and intrinsic differences on either side of the 'mass step' at $10^{10}$ M$\odot$. We find evidence in favour of an intrinsic contribution to the mass step and differing $R_V$ distributions. We also build on recent results supporting an environmental-dependence on the secondary maximum of SNe Ia in $i$-band. Twenty days post-peak, we find an offset in intrinsic $i$-band light curve between each mass bin at a significance in excess of $3\sigma$., Comment: 13 pages, 2 figures, 9 tables. Submitted to MNRAS

Robots need robust and flexible vision systems to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots are commonly deployed in initially unknown environments, pre-training on static datasets cannot always capture the variety of domains and limits the robot's vision performance during missions. Recently, self-supervised as well as fully supervised active learning methods emerged to improve robotic vision. These approaches rely on large in-domain pre-training datasets or require substantial human labelling effort. To address these issues, we present a recent adaptive planning framework for efficient training data collection to substantially reduce human labelling requirements in semantic terrain monitoring missions. To this end, we combine high-quality human labels with automatically generated pseudo labels. Experimental results show that the framework reaches segmentation performance close to fully supervised approaches with drastically reduced human labelling effort while outperforming purely self-supervised approaches. We discuss the advantages and limitations of current methods and outline valuable future research avenues towards more robust and flexible robotic vision systems in unknown environments., Comment: 5 pages, 3 figures

We investigate the effect of a Heaviside cut-off on the front propagation dynamics of the so-called Burgers-FisherKolmogoroff-Petrowskii-Piscounov (Burgers-FKPP) advection-reaction-diffusion equation. We prove the existence and uniqueness of a travelling front solution in the presence of a cut-off in the reaction kinetics and the advection term, and we derive the leading-order asymptotics for the speed of propagation of the front in dependence on the advection strength and the cut-off parameter. Our analysis relies on geometric techniques from dynamical systems theory and specifically, on geometric desingularisation, which also known as blow-up.

Relation extraction is crucial for constructing knowledge graphs, with large high-quality datasets serving as the foundation for training, fine-tuning, and evaluating models. Generative data augmentation (GDA) is a common approach to expand such datasets. However, this approach often introduces hallucinations, such as spurious facts, whose impact on relation extraction remains underexplored. In this paper, we examine the effects of hallucinations on the performance of relation extraction on the document and sentence levels. Our empirical study reveals that hallucinations considerably compromise the ability of models to extract relations from text, with recall reductions between 19.1% and 39.2%. We identify that relevant hallucinations impair the model's performance, while irrelevant hallucinations have a minimal impact. Additionally, we develop methods for the detection of hallucinations to improve data quality and model performance. Our approaches successfully classify texts as either 'hallucinated' or 'clean,' achieving high F1-scores of 83.8% and 92.2%. These methods not only assist in removing hallucinations but also help in estimating their prevalence within datasets, which is crucial for selecting high-quality data. Overall, our work confirms the profound impact of relevant hallucinations on the effectiveness of relation extraction models., Comment: Accepted at KBC-LM@ISWC'24

The local (z=0.0315) AGN Mrk 817, was monitored over more than 500 days with space-borne and ground-based instruments as part of a large international campaign AGN STORM 2. Here, we present a comprehensive analysis of the broad-band continuum variations using detailed modeling of the broad line region (BLR), several types of disk winds classified by their optical depth, and new numerical simulations. We find that diffuse continuum (DC) emission, with additional contributions from strong and broad emission lines, can explain the continuum lags observed in this source during high and low luminosity phases. Disk illumination by the variable X-ray corona contributes only a small fraction of the observed continuum lags. Our BLR models assume radiation pressure-confined clouds distributed over a distance of 2-122 light days. We present calculated mean-emissivity radii of many emission lines, and DC emission, and suggest a simple, transfer-function-dependent method that ties them to cross-correlation lag determinations. We do not find clear indications for large optical depth winds but identify the signature of lower column density winds. In particular, we associate the shortest observed continuum lags with a combination of tau(1 Ryd) approx. 2 wind and a partly shielded BLR. Even smaller optical depth winds may be associated with X-ray absorption features and with noticeable variations in the width and lags of several high ionization lines like HeII and CIV. Finally, we demonstrate the effect of torus dust emission on the observed lags in the i and z bands., Comment: 23 pages, 18 figures. Corrected typographical error in the title of the paper as it appeared in the Metadata

Diabetic foot ulcers (DFUs) are a leading cause of hospitalizations and lower limb amputations, placing a substantial burden on patients and healthcare systems. Early detection and accurate classification of DFUs are critical for preventing serious complications, yet many patients experience delays in receiving care due to limited access to specialized services. Telehealth has emerged as a promising solution, improving access to care and reducing the need for in-person visits. The integration of artificial intelligence and pattern recognition into telemedicine has further enhanced DFU management by enabling automatic detection, classification, and monitoring from images. Despite advancements in artificial intelligence-driven approaches for DFU image analysis, the application of large language models for DFU image transcription has not yet been explored. To address this gap, we introduce UlcerGPT, a novel multimodal approach leveraging large language and vision models for DFU image transcription. This framework combines advanced vision and language models, such as Large Language and Vision Assistant and Chat Generative Pre-trained Transformer, to transcribe DFU images by jointly detecting, classifying, and localizing regions of interest. Through detailed experiments on a public dataset, evaluated by expert clinicians, UlcerGPT demonstrates promising results in the accuracy and efficiency of DFU transcription, offering potential support for clinicians in delivering timely care via telemedicine., Comment: 13 pages, 3 figures, ICPR 2024 Conference (PRHA workshop)

Relative distances between a high-redshift sample of Type Ia supernovae (SNe~Ia), anchored to a low-redshift sample, have been instrumental in drawing insights on the nature of the dark energy driving the accelerated expansion of the universe. A combination (hereafter called SBC) of the SNe~Ia with baryon acoustic oscillations (BAO) from the Dark Energy Spectroscopic Instrument (DESI) and the cosmic microwave background (CMB) recently indicated deviations from the standard interpretation of dark energy as a cosmological constant. In this paper, we analyse various systematic uncertainties in the distance measurement of SNe~Ia and their impact on the inferred dark energy properties in the canonical Chevallier-Polarski-Linder (CPL) model. We model systematic effects like photometric calibration, progenitor and dust evolution, and uncertainty in the galactic extinction law. We find that all the dominant systematic errors shift the dark energy inference towards the DESI 2024 results from an underlying $\Lambda$CDM cosmology. A small change in the calibration, and change in the Milky Way dust, can give rise to systematic-driven shifts on $w_0$-$w_a$ constraints, comparable to the deviation reported from the DESI 2024 results. We forecast that the systematic uncertainties can shift the inference of $w_0-w_a$ by a few times the error ellipse for future low- and high-$z$ SN~Ia compilations and hence, it is critical to circumvent them to robustly test for deviations from $\Lambda$. A slider and visualisation tool for quantifying the impact of systematic effects on the fitted cosmological parameters is publicly available at: https://github.com/sdhawan21/DEslider.git, Comment: 9 pages, 5 figures: see figure 3 for the axis of systematic bias. User interface tool to test the impact of systematic variation is available https://github.com/sdhawan21/DEslider.git

Conditional Flow Matching (CFM) models can generate high-quality samples from a non-informative prior, but they can be slow, often needing hundreds of network evaluations (NFE). To address this, we propose Implicit Dynamical Flow Fusion (IDFF); IDFF learns a new vector field with an additional momentum term that enables taking longer steps during sample generation while maintaining the fidelity of the generated distribution. Consequently, IDFFs reduce the NFEs by a factor of ten (relative to CFMs) without sacrificing sample quality, enabling rapid sampling and efficient handling of image and time-series data generation tasks. We evaluate IDFF on standard benchmarks such as CIFAR-10 and CelebA for image generation, where we achieve likelihood and quality performance comparable to CFMs and diffusion-based models with fewer NFEs. IDFF also shows superior performance on time-series datasets modeling, including molecular simulation and sea surface temperature (SST) datasets, highlighting its versatility and effectiveness across different domains.\href{https://github.com/MrRezaeiUofT/IDFF}{Github Repository}

2D metal halide perovskites have enabled significant stability improvements in perovskite devices, particularly in resistance to moisture. However, some 2D perovskites are even more susceptible to photooxidation compared to 3D perovskites. This is particularly true for more commonly investigated Ruddlesden-Popper (RP) perovskites that exhibit increased susceptibility to photoinduced degradation compared to Dion-Jacobson (DJ) perovskites. Comparisons between different RP and DJ perovskites reveal that this phenomenon cannot be explained by commonly proposed differences in superoxide ion generation, interlayer distance and lattice structural rigidity differences. Instead, the resistance to photooxidation of DJ perovskites can be attributed to decreased likelihood of double deprotonation events (compared to single deprotonation events in RP perovskites) required for the loss of organic cations and the perovskite decomposition. Consequently, DJ perovskites are less susceptible to oxidative degradation (both photo- and electrochemically induced), which leads to improved operational stability of solar cells based on these materials., Comment: Main text: 19 pages, 6 figures, supplementary information: 62 pages, 47 figures

X-ray reverberation mapping is a powerful technique for probing the innermost accretion disk, whereas continuum reverberation mapping in the UV, optical, and infrared (UVOIR) reveals reprocessing by the rest of the accretion disk and broad-line region (BLR). We present the time lags of Mrk 817 as a function of temporal frequency measured from 14 months of high-cadence monitoring from Swift and ground-based telescopes, in addition to an XMM-Newton observation, as part of the AGN STORM 2 campaign. The XMM-Newton lags reveal the first detection of a soft lag in this source, consistent with reverberation from the innermost accretion flow. These results mark the first simultaneous measurement of X-ray reverberation and UVOIR disk reprocessing lags$\unicode{x2013}$effectively allowing us to map the entire accretion disk surrounding the black hole. Similar to previous continuum reverberation mapping campaigns, the UVOIR time lags arising at low temporal frequencies are longer than those expected from standard disk reprocessing by a factor of 2-3. The lags agree with the anticipated disk reverberation lags when isolating short-timescale variability, namely timescales shorter than the H$\beta$ lag. Modeling the lags requires additional reprocessing constrained at a radius consistent with the BLR size scale inferred from contemporaneous H$\beta$-lag measurements. When we divide the campaign light curves, the UVOIR lags show substantial variations, with longer lags measured when obscuration from an ionized outflow is greatest. We suggest that, when the obscurer is strongest, reprocessing by the BLR elongates the lags most significantly. As the wind weakens, the lags are dominated by shorter accretion disk lags., Comment: 28 pages, 11 figures, 3 tables. Accepted for publication in the Astrophysical Journal

We present the first homogeneous release of several thousand Type Ia supernovae (SNe Ia), all having spectroscopic classification, and spectroscopic redshifts for half the sample. This release, named the "DR2", contains 3628 nearby (z < 0.3) SNe Ia discovered, followed and classified by the Zwicky Transient Facility survey between March 2018 and December 2020. Of these, 3000 have good-to-excellent sampling and 2667 pass standard cosmology light-curve quality cuts. This release is thus the largest SN Ia release to date, increasing by an order of magnitude the number of well characterized low-redshift objects. With the "DR2", we also provide a volume-limited (z < 0.06) sample of nearly a thousand SNe Ia. With such a large, homogeneous and well controlled dataset, we are studying key current questions on SN cosmology, such as the linearity SNe Ia standardization, the SN and host dependencies, the diversity of the SN Ia population, and the accuracy of the current light-curve modeling. These, and more, are studied in detail in a series of articles associated with this release. Alongside the SN Ia parameters, we publish our force-photometry gri-band light curves, 5138 spectra, local and global host properties, observing logs, and a python tool to ease use and access of these data. The photometric accuracy of the "DR2" is not yet suited for cosmological parameter inference, which will follow as "DR2.5" release. We nonetheless demonstrate that the multi-thousand SN Ia Hubble Diagram has a typical 0.15 mag scatter., Comment: ZTF SN Ia DR2 release paper. Accepted for publication (Astronomy and Astrophysics). Reference paper for the ZTF SN Ia DR2 A&A Special Issue

The Zwicky Transient Facility SN Ia Data Release 2 (ZTF SN Ia DR2) contains more than 3,000 Type Ia supernovae (SNe Ia), providing the largest homogeneous low-redshift sample of SNe Ia. Having at least one spectrum per event, this data collection is ideal for large-scale statistical studies of the photometric, spectroscopic and host-galaxy properties of SNe Ia, particularly of the more rare "peculiar" subclasses. In this paper, we first present the method we developed to spectroscopically classify the SNe in the sample, and the techniques we used to model their multi-band light curves and explore their photometric properties. We then show a method to distinguish between the "peculiar" subtypes and the normal SNe Ia. We also explore the properties of their host galaxies and estimate their relative rates, focusing on the "peculiar" subtypes and their connection to the cosmologically useful SNe Ia. Finally, we discuss the implications of our study with respect to the progenitor systems of the "peculiar" SN Ia events., Comment: 19 pages, 13 figures, submitted to Astronomy and Astrophysics

Using 1533 type Ia supernovae (SNe Ia) from the five-year sample of the Dark Energy Survey (DES), we investigate the effects of projected galactocentric separation between the SNe and their host galaxies on their light curves and standardization. We show, for the first time, that the difference in SN Ia post-standardization brightnesses between high and low-mass hosts reduces from $0.078\pm0.011$ mag in the full sample to $0.036 \pm 0.018$ mag for SNe Ia located in the outer regions of their host galaxies, while increasing to $0.100 \pm 0.014$ mag for SNe in the inner regions. In these inner regions, the step can be reduced (but not removed) using a model where the $R_V$ of dust along the line-of-sight to the SN changes as a function of galaxy properties. To explain the remaining difference, we use the distributions of the SN Ia stretch parameter to test whether the inferred age of SN progenitors are more varied in the inner regions of galaxies. We find that the proportion of high-stretch SNe Ia in red (older) environments is more prominent in outer regions and that the outer regions stretch distributions are overall more homogeneous compared to inner regions, but conclude that this effect cannot explain the reduction in significance of any Hubble residual step in outer regions. We conclude that the standardized distances of SNe Ia located in the outer regions of galaxies are less affected by their global host galaxy properties than those in the inner regions., Comment: 17 pages, 13 figures

The extremes of Active Galactic Nuclei (AGN) variability offer valuable new insights into the drivers and physics of AGN. We discuss some of the most extreme cases of AGN variability; the highest amplitudes, deep minima states, extreme spectral states, Seyfert-type changes, and semi-periodic signals, including new X-ray observations. The properties of changing-look (CL) AGN are briefly reviewed and a classification scheme is proposed which encompasses the variety of CL phenomena; distinguishing slow and fast events, repeat events, and frozen-look AGN which do not show any emission-line response. Long-term light curves that are densely covered over multiple years, along with follow-up spectroscopy, are utilized to gain insight into the underlying variability mechanisms including accretion disk and broad-line region physics. Remarkable differences are seen, for instance, in the optical spectral response to extreme outbursts, implying distinct intrinsic variability mechanisms. Furthermore, we discuss methods for distinguishing between CL AGN and CL look-alike events (tidal disruption events or supernovae in dense media). Finally, semi-periodic light curve variability is addressed and the latest multiwavelength (MWL) light curve of the binary supermassive black hole (SMBH) candidate OJ 287 from the MOMO project is presented. Recent results from that project have clearly established the need for new binary SMBH modelling matching the tight new constraints from observations, including the measurement of a low (primary) SMBH mass of ~10^8 Msun which also implies that OJ 287 is no longer in the regime of near-future pulsar timing arrays., Comment: Submitted to Advances in Space Research on July 11 (Special Issue on 'Astrophysical Spectroscopy and Data in Investigation of Laboratory and Space Plasmas'). Based on an invited talk given at the VI. Conference on AGN and Gravitational Lensing

This is the preliminary ranking of WMT24 General MT systems based on automatic metrics. The official ranking will be a human evaluation, which is superior to the automatic ranking and supersedes it. The purpose of this report is not to interpret any findings but only provide preliminary results to the participants of the General MT task that may be useful during the writing of the system submission.

In this paper, we study and compare the optical and X-ray counterparts of subparsec supermassive black hole binaries (SMBHBs). With that aim, we simulated the profiles of optical spectral lines emitted from the broad line region (BLR) as well as X-ray spectral lines emitted from the relativistic accretion disks around both black holes and compared them with each other. The obtained results showed that SMBHBs could cause a specific, but different variability of the lines from the optical part and Fe K$\alpha$ line, leaving potentially detectable imprints in their profiles. Since these imprints depend on the orbital phase of the system, they could be used for reconstructing the Keplerian orbits of the components in the observed SMBHBs. Moreover, such signatures in the optical and X-ray line profiles of the observed SMBHBs could be used as a tool for the detection of these objects as well as for studying their properties., Comment: 18 pages, 1 table, 10 figures. Accepted for publication in Advances in Space Research

Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance-redshift relation. While obtaining a host-galaxy spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of millions of SNe. Here we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts (photo-z) inferred directly from the SN light-curve in combination with Gaussian and full p(z) priors from host-galaxy photo-z estimates. Using the DES 5-year photometrically-classified SN sample, we consider several photo-z algorithms as host-galaxy photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional-Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3x2 point analyses. With detailed catalog-level simulations of the DES 5-year sample, we find that the simulated w can be recovered within $\pm$0.02 when using SN+SOMPZ or DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases in w consistent with simulations within ~1$\sigma$ for three of the five photo-z variants. We further evaluate how photo-z systematics interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays the foundation for next-generation fully photometric SNe Ia cosmological analyses., Comment: 19 pages, 9 figures. Submitting to MNRAS, comments welcome

There exists an invisible barrier between healthcare professionals' perception of a patient's clinical experience and the reality. This barrier may be induced by the environment that hinders patients from sharing their experiences openly with healthcare professionals. As patients are observed to discuss and exchange knowledge more candidly on social media, valuable insights can be leveraged from these platforms. However, the abundance of non-patient posts on social media necessitates filtering out such irrelevant content to distinguish the genuine voices of patients, a task we refer to as patient voice classification. In this study, we analyse the importance of linguistic characteristics in accurately classifying patient voices. Our findings underscore the essential role of linguistic and statistical text similarity analysis in identifying common patterns among patient groups. These results allude to even starker differences in the way patients express themselves at a disease level and across various therapeutic domains. Additionally, we fine-tuned a pre-trained Language Model on the combined datasets with similar linguistic patterns, resulting in a highly accurate automatic patient voice classification. Being the pioneering study on the topic, our focus on extracting authentic patient experiences from social media stands as a crucial step towards advancing healthcare standards and fostering a patient-centric approach., Comment: 14 pages, 4 figures, 5 tables, funded by Talking Medicines Limited

We observed the Seyfert 1 galaxy Mrk817 during an intensive multi-wavelength reverberation mapping campaign for 16 months. Here, we examine the behavior of narrow UV absorption lines seen in HST/COS spectra, both during the campaign and in other epochs extending over 14 years. We conclude that while the narrow absorption outflow system (at -3750 km/s with FWHM=177 km/s) responds to the variations of the UV continuum as modified by the X-ray obscurer, its total column density (logNH =19.5 cm-2) did not change across all epochs. The adjusted ionization parameter (scaled with respect to the variations in the Hydrogen ionizing continuum flux) is log UH =-1.0. The outflow is located at a distance smaller than 38 parsecs from the central source, which implies a hydrogen density of nH > 3000 cm-3. The absorption outflow system only covers the continuum emission source and not the broad emission line region, which suggests that its transverse size is small (< 1e16 cm), with potential cloud geometries ranging from spherical to elongated along the line of sight., Comment: 18 pages, 12 Figures, Accepted for publication in ApJ

Presenting analytical results for Binary Media-Based Modulation (B-MBM) over fading channels for single-antenna receivers. Illustrating that open-loop B-MBM, in the absence of feedback, only achieves a diversity order of one. However, with feedback and optimal weight selection in closed-loop configurations, a diversity order of two becomes achievable. Notably, the closed-loop B-MBM, with analytically computed optimal weights, performs equivalent to Alamouti-coded BPSK transmission, demonstrating feasibility even with just one radio frequency chain when feedback is available., Comment: 5 pages, 4 figures, 1 table

ESO is in the process of upgrading one of the two FORS (FOcal Reducer/low dispersion Spectrograph) instruments - a multi-mode (imaging, polarimetry, long-slit, and multi-object spectroscopy) optical instrument mounted on the Cassegrain focus of Unit Telescope 1 of ESO's Very Large Telescope. FORS1 was moved from Chile to Trieste, and is undergoing complete refurbishment, including the exchange of all motorised parts. In addition, new software is developed, based on the Extremely Large Telescope Instrument Control Software Framework, as the upgraded FORS1 will be the first instrument in operations to use this framework. The new Teledyne e2V CCD has now been procured and is undergoing testing with the New Generation Controller at ESO. In addition, a new set of grisms have been developed, and a new set of filters will be purchased. A new internal calibration unit has been designed, making the operations more efficient., Comment: This paper is dedicated to the memory of our former colleague, Mario Nonino, who passed away prematurely. Proceedings for the SPIE Astronomical Telescopes and Instrumentation 2024

Recently, Python Testbed for Federated Learning Algorithms emerged as a low code and generative large language models amenable framework for developing decentralized and distributed applications, primarily targeting edge systems, by nonprofessional programmers with the help of emerging artificial intelligence tools. This light framework is written in pure Python to be easy to install and to fit into a small IoT memory. It supports formally verified generic centralized and decentralized federated learning algorithms, as well as the peer-to-peer data exchange used in time division multiplexing communication, and its current main limitation is that all the application instances can run only on a single PC. This paper presents the MicroPyton Testbed for Federated Learning Algorithms, the new framework that overcomes its predecessor's limitation such that individual application instances may run on different network nodes like PCs and IoTs, primarily in edge systems. The new framework carries on the pure Python ideal, is based on asynchronous I/O abstractions, and runs on MicroPython, and therefore is a great match for IoTs and devices in edge systems. The new framework was experimentally validated on a wireless network comprising PCs and Raspberry Pi Pico W boards, by using application examples originally developed for the predecessor framework., Comment: 20 pages, 6 figures, 12 tables, the extended paper preprint