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The impact of voice disorders is becoming more widely acknowledged as a public health issue. Several machine learning-based classifiers with the potential to identify disorders have been used in recent studies to differentiate between normal and pathological voices and sounds. In this paper, we focus on analyzing the vulnerabilities of these systems by exploring the possibility of attacks that can reverse classification and compromise their reliability. Given the critical nature of personal health information, understanding which types of attacks are effective is a necessary first step toward improving the security of such systems. Starting from the original audios, we implement various attack methods, including adversarial, evasion, and pitching techniques, and evaluate how state-of-the-art disorder detection models respond to them. Our findings identify the most effective attack strategies, underscoring the need to address these vulnerabilities in machine-learning systems used in the healthcare domain., Comment: 7 pages, 17 figures, accepted for 16th IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS) 2024

3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to distinct application scenarios. These assumptions limit their use when acquisition conditions, such as the subject's distance from the camera or the camera's characteristics, are different than expected, as typically happens in video surveillance. Additionally, 3DFR algorithms follow various strategies to address the reconstruction of a 3D shape from 2D data, such as statistical model fitting, photometric stereo, or deep learning. In the present study, we explore the application of three 3DFR algorithms representative of the SOTA, employing each one as the template set generator for a face verification system. The scores provided by each system are combined by score-level fusion. We show that the complementarity induced by different 3DFR algorithms improves performance when tests are conducted at never-seen-before distances from the camera and camera characteristics (cross-distance and cross-camera settings), thus encouraging further investigations on multiple 3DFR-based approaches., Comment: Accepted at T-CAP - Towards a Complete Analysis of People: Fine-grained Understanding for Real-World Applications, workshop in conjunction with the 18th European Conference on Computer Vision ECCV 2024

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.

Understanding the origin of fast radio bursts (FRBs) has become the main science driver of recent dedicated FRB surveys. Between July 2019 and February 2022, we carried out ALERT, an FRB survey at 1370 MHz using the Apertif instrument installed at the Westerbork Synthesis Radio Telescope (WSRT). Here we report the detection of 18 new FRBs, and we study the properties of the entire 24 burst sample detected during the survey. For five bursts, we identify host galaxy candidates with >50% probability association. We observe an average linear polarisation fraction of $\sim$43% and an average circular polarisation fraction consistent with 0%. A third of the FRBs display multiple components. The sample next reveals a population of highly scattered bursts, which is most likely to have been produced in the immediate circumburst environment. Furthermore, two FRBs show evidence for high rotation measures, reaching |RM|>$10^3$ rad m$^{-2}$ in the source reference frames. Together, the scattering and rotation measures ALERT finds prove that a large fraction of FRBs are embedded in complex media such as star forming regions or supernova remnants. Through the discovery of the third most dispersed FRB so far, we show that one-off FRBs can emit at frequencies in excess of 6 GHz. Finally, we determine an FRB all-sky rate of $459^{+208}_{-155}$ sky$^{-1}$ day$^{-1}$ above a fluence limit of 4.1 Jy ms, and a fluence cumulative distribution with a power law index $\gamma=-1.23\pm0.06\pm0.2$, which is roughly consistent with the Euclidean Universe predictions. Through the high resolution in time, frequency, polarisation and localisation that ALERT featured, we were able to determine the morphological complexity, polarisation, local scattering and magnetic environment, and high-frequency luminosity of FRBs. We find all these strongly resemble those seen in young, energetic, highly magnetised neutron stars., Comment: 40 pages (including 11 of appendix), 37 figures, 5 tables. Accepted for publication in A&A

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussed., Comment: The 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024)

We present deep total intensity and polarization observations of the Coma cluster at 1.4 and 6.6 GHz performed with the Sardinia Radio Telescope. By combining the single-dish 1.4 GHz data with archival Very Large Array observations we obtain new images of the central radio halo and of the peripheral radio relic where we properly recover the brightness from the large scale structures. At 6.6 GHz we detect both the relic and the central part of the halo in total intensity and polarization. These are the highest frequency images available to date for these radio sources in this galaxy cluster. In the halo, we find a localized spot of polarized signal, with fractional polarization of about 45%. The polarized emission possibly extends along the north-east side of the diffuse emission. The relic is highly polarized, up to 55%, as usually found for these sources. We confirm the halo spectrum is curved, in agreement with previous single-dish results. The spectral index is alpha=1.48 +/- 0.07 at a reference frequency of 1 GHz and varies from alpha ~1.1, at 0.1 GHz, up to alpha ~ 1.8, at 10 GHz. We compare the Coma radio halo surface brightness profile at 1.4 GHz (central brightness and e-folding radius) with the same properties of the other halos, and we find that it has one of the lowest emissivities observed so far. Reanalyzing the relic's spectrum in the light of the new data, we obtain a refined radio Mach number of M=2.9 +/- 0.1., Comment: 27 pages, 18 figures, this article has been accepted for publication in MNRAS Published by Oxford University Press on behalf of the Royal Astronomical Society

Remarkable progress has been made in recent years in the fields of vision, language, and robotics. We now have vision models capable of recognizing objects based on language queries, navigation systems that can effectively control mobile systems, and grasping models that can handle a wide range of objects. Despite these advancements, general-purpose applications of robotics still lag behind, even though they rely on these fundamental capabilities of recognition, navigation, and grasping. In this paper, we adopt a systems-first approach to develop a new Open Knowledge-based robotics framework called OK-Robot. By combining Vision-Language Models (VLMs) for object detection, navigation primitives for movement, and grasping primitives for object manipulation, OK-Robot offers a integrated solution for pick-and-drop operations without requiring any training. To evaluate its performance, we run OK-Robot in 10 real-world home environments. The results demonstrate that OK-Robot achieves a 58.5% success rate in open-ended pick-and-drop tasks, representing a new state-of-the-art in Open Vocabulary Mobile Manipulation (OVMM) with nearly 1.8x the performance of prior work. On cleaner, uncluttered environments, OK-Robot's performance increases to 82%. However, the most important insight gained from OK-Robot is the critical role of nuanced details when combining Open Knowledge systems like VLMs with robotic modules. Videos of our experiments and code are available on our website: https://ok-robot.github.io, Comment: Github repo: https://github.com/ok-robot/ok-robot

The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD). This edition, LivDet2023, proposed two challenges, Liveness Detection in Action and Fingerprint Representation, to evaluate the efficacy of PAD embedded in verification systems and the effectiveness and compactness of feature sets. A third, hidden challenge is the inclusion of two subsets in the training set whose sensor information is unknown, testing participants ability to generalize their models. Only bona fide fingerprint samples were provided to participants, and the competition reports and assesses the performance of their algorithms suffering from this limitation in data availability., Comment: 9 pages, 10 tables, IEEE International Joint Conference on Biometrics (IJCB 2023)

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them., Comment: The manuscript has been accepted for publication in ACM Computing Surveys. arXiv admin note: text overlap with arXiv:2303.11164

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role in bringing evidence to a lawsuit. Shedding some light on this matter is the goal of the present survey, where we start by clarifying the relation between forensic applications and biometrics. To our knowledge, no previous work adopted this relation to make the point on the state of the art. Therefore, we analyzed the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discussed the current obstacles that separate 3D face reconstruction from an active role in forensic applications.

Context. Apertif is a multi-beam receiver system for the Westerbork Synthesis Radio Telescope that operates at 1.1-1.5 GHz, which overlaps with various radio services, resulting in contamination of astronomical signals with radio-frequency interference (RFI). Aims. We analyze approaches to mitigate Apertif interference and design an automated detection procedure for its imaging mode. Using this approach, we present long-term RFI detection results of over 300 Apertif observations. Methods. Our approach is based on the AOFlagger detection approach. We introduce several new features, including ways to deal with ranges of invalid data (e.g. caused by shadowing) in both the SumThreshold and scale-invariant rank operator steps; pre-calibration bandpass calibration; auto-correlation flagging; and HI flagging avoidance. These methods are implemented in a new framework that uses the Lua language for scripting, which is new in AOFlagger version 3. Results. Our approach removes RFI fully automatically, and is robust and effective enough for further calibration and (continuum) imaging of these data. Analysis of 304 observations show an average of 11.1% of lost data due to RFI with a large spread. We observe 14.6% RFI in auto-correlations. Computationally, AOFlagger achieves a throughput of 370 MB/s on a single computing node. Compared to published machine learning results, the method is one to two orders of magnitude faster., Comment: 15 pages, 10 figures. Accepted for publication in A&A

This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly available satellite imagery and vector shapes coming from a large corpus of annotations (i.e., surveyed sites). A randomized test showed that the best model reaches a detection accuracy in the neighborhood of 80%. Integrating domain expertise was crucial to define how to build the dataset and how to evaluate the predictions, since defining if a proposed mask counts as a prediction is very subjective. Furthermore, even an inaccurate prediction can be useful when put into context and interpreted by a trained archaeologist. Coming from these considerations we close the paper with a vision for a Human-AI collaboration workflow. Starting with an annotated dataset that is refined by the human expert we obtain a model whose predictions can either be combined to create a heatmap, to be overlaid on satellite and/or aerial imagery, or alternatively can be vectorized to make further analysis in a GIS software easier and automatic. In turn, the archaeologists can analyze the predictions, organize their onsite surveys, and refine the dataset with new, corrected, annotation, Comment: 15 pages, 5 figures, 2 tables

(Abridged) Apertif is a phased-array feed system for WSRT, providing forty instantaneous beams over 300 MHz of bandwidth. A dedicated survey program started on 1 July 2019, with the last observations taken on 28 February 2022. We describe the release of data products from the first year of survey operations, through 30 June 2020. We focus on defining quality control metrics for the processed data products. The Apertif imaging pipeline, Apercal, automatically produces non-primary beam corrected continuum images, polarization images and cubes, and uncleaned spectral line and dirty beam cubes for each beam of an Apertif imaging observation. For this release, processed data products are considered on a beam-by-beam basis within an observation. We validate the continuum images by using metrics that identify deviations from Gaussian noise in the residual images. If the continuum image passes validation, we release all processed data products for a given beam. We apply further validation to the polarization and line data products. We release all raw observational data from the first year of survey observations, for a total of 221 observations of 160 independent target fields, covering approximately one thousand square degrees of sky. Images and cubes are released on a per beam basis, and 3374 beams are released. The median noise in the continuum images is 41.4 uJy/bm, with a slightly lower median noise of 36.9 uJy/bm in the Stokes V polarization image. The median angular resolution is 11.6"/sin(Dec). The median noise for all line cubes, with a spectral resolution of 36.6 kHz, is 1.6 mJy/bm, corresponding to a 3-sigma HI column density sensitivity of 1.8 x 10^20 atoms cm^-2 over 20 km/s (for a median angular resolution of 24" x 15"). We also provide primary beam images for each individual Apertif compound beam. The data are made accessible using a Virtual Observatory interface., Comment: Accepted for publication in A&A, updated Figure 1

We measure the local correlation between radio emission and Compton-$y$ signal across two galaxy clusters, Abell~399 and Abell~401, using maps from the Low-Frequency Array (LOFAR) and the Atacama Cosmology Telescope (ACT) + \Planck. These datasets allow us to make the first measurement of this kind at $\sim$arcminute resolution. We find that the radio brightness scales as $F_{\mathrm{radio}} \propto y^{1.5}$ for Abell~401 and $F_{\mathrm{radio}} \propto y^{2.8}$ for Abell~399. Furthermore, using \XMM data, we derive a sublinear correlation between radio and X-ray brightness for both the clusters ($F_{\mathrm{radio}} \propto F_{\rm X}^{0.7}$). Finally, we correlate the Compton-$y$ and X-ray data, finding that an isothermal model is consistent with the cluster profiles, $y \propto F_{\rm X}^{0.5}$. By adopting an isothermal--$\beta$ model, we are able, for the first time, to jointly use radio, X-ray, and Compton-$y$ data to estimate the scaling index for the magnetic field profile, $B(r) \propto n_{\mathrm{e}}(r)^{\eta}$ in the injection and re-acceleration scenarios. Applying this model, we find that the combined radio and Compton-$y$ signal exhibits a significantly tighter correlation with the X-ray across the clusters than when the datasets are independently correlated. We find $\eta \sim 0.6{-}0.8$. These results are consistent with the upper limit we derive for the scaling index of the magnetic field using rotation measure values for two radio galaxies in Abell~401. We also measure the radio, Compton-$y$, and X-ray correlations in the filament between the clusters but conclude that deeper data are required for a convincing determination of the correlations in the filament., Comment: 16 pages, 8 figures, Accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal

Fast Radio Bursts must be powered by uniquely energetic emission mechanisms. This requirement has eliminated a number of possible source types, but several remain. Identifying the physical nature of Fast Radio Burst (FRB) emitters arguably requires good localisation of more detections, and broadband studies enabled by real-time alerting. We here present the Apertif Radio Transient System (ARTS), a supercomputing radio-telescope instrument that performs real-time FRB detection and localisation on the Westerbork Synthesis Radio Telescope (WSRT) interferometer. It reaches coherent-addition sensitivity over the entire field of the view of the primary dish beam. After commissioning results verified the system performed as planned, we initiated the Apertif FRB survey (ALERT). Over the first 5 weeks we observed at design sensitivity in 2019, we detected 5 new FRBs, and interferometrically localised each of these to 0.4--10 sq. arcmin. All detections are broad band and very narrow, of order 1 ms duration, and unscattered. Dispersion measures are generally high. Only through the very high time and frequency resolution of ARTS are these hard-to-find FRBs detected, producing an unbiased view of the intrinsic population properties. Most localisation regions are small enough to rule out the presence of associated persistent radio sources. Three FRBs cut through the halos of M31 and M33. We demonstrate that Apertif can localise one-off FRBs with an accuracy that maps magneto-ionic material along well-defined lines of sight. The rate of 1 every ~7 days next ensures a considerable number of new sources are detected for such study. The combination of detection rate and localisation accuracy exemplified by the 5 first ARTS FRBs thus marks a new phase in which a growing number of bursts can be used to probe our Universe., Comment: Accepted version

Ionized gas probes the influence of massive stars on their environment. The Cygnus X region (d~1.5 kpc) is one of the most massive star forming complexes in our Galaxy, in which the Cyg OB2 association (age of 3-5 Myr and stellar mass $2 \times 10^{4}$ M$_{\odot}$) has a dominant influence. We observe the Cygnus X region at 148 MHz using the Low Frequency Array (LOFAR) and take into account short-spacing information during image deconvolution. Together with data from the Canadian Galactic Plane Survey, we investigate the morphology, distribution, and physical conditions of low-density ionized gas in a $4^{\circ} \times 4^{\circ}$ (100 pc $\times$ 100 pc) region at a resolution of 2' (0.9 pc). The Galactic radio emission in the region analyzed is almost entirely thermal (free-free) at 148 MHz, with emission measures of $10^3 < EM~{\rm[pc~cm^{-6}]} < 10^6$. As filamentary structure is a prominent feature of the emission, we use DisPerSE and FilChap to identify filamentary ridges and characterize their radial ($EM$) profiles. The distribution of radial profiles has a characteristic width of 4.3 pc and a power-law distribution ($\beta = -1.8 \pm 0.1$) in peak $EM$ down to our completeness limit of 4200 pc cm$^{-6}$. The electron densities of the filamentary structure range from $10 < n_e~{\rm[cm^{-3}]} < 400$ with a median value of 35 cm$^{-3}$, remarkably similar to [N II] surveys of ionized gas. Cyg OB2 may ionize at most two-thirds of the total ionized gas and the ionized gas in filaments. More than half of the filamentary structures are likely photoevaporating surfaces flowing into a surrounding diffuse (~5 cm$^{-3}$) medium. However, this is likely not the case for all ionized gas ridges. A characteristic width in the distribution of ionized gas points to the stellar winds of Cyg OB2 creating a fraction of the ionized filaments through swept-up ionized gas or dissipated turbulence., Comment: 19 pages, 14 figures, 1 table; accepted for publication in A&A

We analyse five early science datasets from the APERture Tile in Focus (Apertif) phased array feed system to verify the polarisation capabilities of Apertif in view of future larger data releases. We aim to characterise the source population of the polarised sky in the L-Band using polarised source information in combination with IR and optical data. We use automatic routines to generate full field-of-view Q- and U-cubes and perform RM-Synthesis, source finding, and cross-matching with published radio, optical, and IR data to generate polarised source catalogues. SED-fitting routines were used to determine photometric redshifts, star-formation rates, and galaxy masses. IR colour information was used to classify sources as AGN or star-forming-dominated and early- or late-type. We surveyed an area of 56deg$^2$ and detected 1357 polarised source components in 1170 sources. The fraction of polarised sources is 10.57% with a median fractional polarisation of 4.70$\pm$0.14%. We confirmed the reliability of the Apertif measurements by comparing them with polarised cross-identified NVSS sources. Average RMs of the individual fields lie within the error of the best Milky Way foreground measurements. All of our polarised sources were found to be dominated by AGN activity in the radio regime with most of them being radio-loud (79%) and of the FRII class (87%). The host galaxies of our polarised source sample are dominated by intermediate disc and star-forming disc galaxies. The contribution of star formation to the radio emission is on the order of a few percent for $\approx$10% of the polarised sources while for $\approx$90% it is completely dominated by the AGN. We do not see any change in fractional polarisation for different star-formation rates of the AGN host galaxies., Comment: 24 pages, 21 figures

In this data release from the LOFAR Two-metre Sky Survey (LoTSS) we present 120-168MHz images covering 27% of the northern sky. Our coverage is split into two regions centred at approximately 12h45m +44$^\circ$30' and 1h00m +28$^\circ$00' and spanning 4178 and 1457 square degrees respectively. The images were derived from 3,451hrs (7.6PB) of LOFAR High Band Antenna data which were corrected for the direction-independent instrumental properties as well as direction-dependent ionospheric distortions during extensive, but fully automated, data processing. A catalogue of 4,396,228 radio sources is derived from our total intensity (Stokes I) maps, where the majority of these have never been detected at radio wavelengths before. At 6" resolution, our full bandwidth Stokes I continuum maps with a central frequency of 144MHz have: a median rms sensitivity of 83$\mu$Jy/beam; a flux density scale accuracy of approximately 10%; an astrometric accuracy of 0.2"; and we estimate the point-source completeness to be 90% at a peak brightness of 0.8mJy/beam. By creating three 16MHz bandwidth images across the band we are able to measure the in-band spectral index of many sources, albeit with an error on the derived spectral index of +/-0.2 which is a consequence of our flux-density scale accuracy and small fractional bandwidth. Our circular polarisation (Stokes V) 20" resolution 120-168MHz continuum images have a median rms sensitivity of 95$\mu$Jy/beam, and we estimate a Stokes I to Stokes V leakage of 0.056%. Our linear polarisation (Stokes Q and Stokes U) image cubes consist of 480 x 97.6 kHz wide planes and have a median rms sensitivity per plane of 10.8mJy/beam at 4' and 2.2mJy/beam at 20"; we estimate the Stokes I to Stokes Q/U leakage to be approximately 0.2%. Here we characterise and publicly release our Stokes I, Q, U and V images in addition to the calibrated uv-data., Comment: 23 figures, 1 table and 29 pages. The catalogues, images and uv-data associated with this data release are publicly available via https://lofar-surveys.org/

The aim of this work is to make a further step towards the understanding of the near-critical reflection of internal gravity waves from a slope in the more general and realistic context where the size of viscosity $\nu$ and the size of diffusivity $\kappa$ are different. In particular, we provide a systematic characterization of boundary layers (boundary layer wave packets) decays and sizes depending on the order of magnitude of viscosity and diffusivity. We can construct an $L^2$ stable approximate solution to the linear near-critical reflection problem under the scaling assumption of Dauxois \& Young JFM 1999, where either viscosity of diffusivity satisfies a precise scaling law in terms of the criticality parameter., Comment: Proceeding of the INdAM Workshop "Analysis and Numerics of Design, Control and Inverse Problems", Rome (Italy), June 28 - July 2, 2021

Fingerprint authentication systems are highly vulnerable to artificial reproductions of fingerprint, called fingerprint presentation attacks. Detecting presentation attacks is not trivial because attackers refine their replication techniques from year to year. The International Fingerprint liveness Detection Competition (LivDet), an open and well-acknowledged meeting point of academies and private companies that deal with the problem of presentation attack detection, has the goal to assess the performance of fingerprint presentation attack detection (FPAD) algorithms by using standard experimental protocols and data sets. Each LivDet edition, held biannually since 2009, is characterized by a different set of challenges against which competitors must be dealt with. The continuous increase of competitors and the noticeable decrease in error rates across competitions demonstrate a growing interest in the topic. This paper reviews the LivDet editions from 2009 to 2021 and points out their evolution over the years., Comment: Chapter of the Handbook of Biometric Anti-Spoofing (Third Edition)

The galaxy cluster Abell 523 (A523) hosts an extended diffuse synchrotron source historically classified as a radio halo. Its radio power at 1.4 GHz makes it one of the most significant outliers in the scaling relations between observables derived from multi-wavelength observations of galaxy clusters: it has a morphology that is different and offset from the thermal gas, and it has polarized emission at 1.4 GHz typically difficult to observe for this class of sources. A magnetic field fluctuating on large spatial scales (~ 1 Mpc) can explain these peculiarities but the formation mechanism for this source is not yet completely clear. To investigate its formation mechanism, we present new observations obtained with the LOw Frequency ARray at 120-168 MHz and the Jansky Very Large Array at 1-2 GHz, which allow us to study the spectral index distribution of this source. According to our data the source is observed to be more extended at 144 MHz than previously inferred at 1.4 GHz, with a total size of about 1.8 Mpc and a flux density S_144MHz = (1.52 +- 0.31) Jy. The spectral index distribution of the source is patchy with an average spectral index alpha ~ 1.2 between 144 MHz and 1.410 GHz, while an integrated spectral index alpha ~ 2.1 has been obtained between 1.410 GHz and 1.782 GHz. A previously unseen patch of steep spectrum emission is clearly detected at 144 MHz in the south of the cluster. Overall, our findings suggest that we are observing an overlapping of different structures, powered by the turbulence associated with the primary and a possible secondary merger., Comment: 19 pages, 11 figures, accepted for publication on MNRAS

We give sufficient conditions for the well-posedness in $\mathcal{C}^\infty$ of the Cauchy problem for third order equations with time dependent coefficients.

Apertif (APERture Tile In Focus) is one of the Square Kilometre Array (SKA) pathfinder facilities. The Apertif project is an upgrade to the 50-year-old Westerbork Synthesis Radio Telescope (WSRT) using phased-array feed technology. The new receivers create 40 individual beams on the sky, achieving an instantaneous sky coverage of 6.5 square degrees. The primary goal of the Apertif Imaging Survey is to perform a wide survey of 3500 square degrees (AWES) and a medium deep survey of 350 square degrees (AMES) of neutral atomic hydrogen (up to a redshift of 0.26), radio continuum emission and polarisation. Each survey pointing yields 4.6 TB of correlated data. The goal of Apercal is to process this data and fully automatically generate science ready data products for the astronomical community while keeping up with the survey observations. We make use of common astronomical software packages in combination with Python based routines and parallelisation. We use an object oriented module-based approach to ensure easy adaptation of the pipeline. A Jupyter notebook based framework allows user interaction and execution of individual modules as well as a full automatic processing of a complete survey observation. If nothing interrupts processing, we are able to reduce a single pointing survey observation on our five node cluster with 24 physical cores and 256 GB of memory each within 24h keeping up with the speed of the surveys. The quality of the generated images is sufficient for scientific usage for 44 % of the recorded data products with single images reaching dynamic ranges of several thousands. Future improvements will increase this percentage to over 80 %. Our design allowed development of the pipeline in parallel to the commissioning of the Apertif system.