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The assembly of galaxy clusters is understood to be a hierarchical process with a continuous accretion of galaxies over time, which increases the cluster size and mass. Late-type galaxies that fall into clusters can undergo ram-pressure stripping, forming extended gas tails within which star formation can happen. The number, location, and tail orientations of such galaxies provide clues about the galaxy infall process, the assembly of the cluster over time, and the consequences of infall for galaxy evolution. Here, we utilise the $\sim$ 0.5 degree diameter circular field of the Ultraviolet Imaging Telescope to image six galaxy clusters at z < 0.06 that are known to contain jellyfish galaxies. We searched for stripping candidates in the ultraviolet images of these clusters, which revealed 54 candidates showing signs of unilateral extra-planar emission, due to ram-pressure stripping. Seven candidates had already been identified as likely stripping based on optical B-band imaging. We identified 47 new candidates through UV imaging. Spectroscopic redshift information is available for 39 of these candidate galaxies, of which 19 are associated with six clusters. The galaxies with spectroscopic redshifts that are not part of the clusters appear to be within structures at different redshifts identified as additional peaks in the redshift distribution of galaxies, indicating that they might be ram-pressure stripped or disturbed galaxies in other structures along the line of sight. We examine the orbital history of these galaxies based on their location in the position-velocity phase-space diagram and explore a possible connection to the orientation of the tail direction among cluster member candidates. The tails of confirmed cluster member galaxies are found to be oriented away from the cluster centre., Comment: Accepted for publication in A&A

Colorway creation is the task of generating textile samples in alternate color variations maintaining an underlying pattern. The individuation of a suitable color palette for a colorway is a complex creative task, responding to client and market needs, stylistic and cultural specifications, and mood. We introduce a modification of this task, the "generative colorway" creation, that includes minimal shape modifications, and propose a framework, "ColorwAI", to tackle this task using color disentanglement on StyleGAN and Diffusion. We introduce a variation of the InterfaceGAN method for supervised disentanglement, ShapleyVec. We use Shapley values to subselect a few dimensions of the detected latent direction. Moreover, we introduce a general framework to adopt common disentanglement methods on any architecture with a semantic latent space and test it on Diffusion and GANs. We interpret the color representations within the models' latent space. We find StyleGAN's W space to be the most aligned with human notions of color. Finally, we suggest that disentanglement can solicit a creative system for colorway creation, and evaluate it through expert questionnaires and creativity theory.

Current Motor Imagery Brain-Computer Interfaces (MI-BCI) require a lengthy and monotonous training procedure to train both the system and the user. Considering many users struggle with effective control of MI-BCI systems, a more user-centered approach to training might help motivate users and facilitate learning, alleviating inefficiency of the BCI system. With the increase of BCI-controlled games, researchers have suggested using game principles for BCI training, as games are naturally centered on the player. This review identifies and evaluates the application of game design elements to MI-BCI training, a process known as gamification. Through a systematic literature search, we examined how MI-BCI training protocols have been gamified and how specific game elements impacted the training outcomes. We identified 86 studies that employed gamified MI-BCI protocols in the past decade. The prevalence and reported effects of individual game elements on user experience and performance were extracted and synthesized. Results reveal that MI-BCI training protocols are most often gamified by having users move an avatar in a virtual environment that provides visual feedback. Furthermore, in these virtual environments, users were provided with goals that guided their actions. Using gamification, the reviewed protocols allowed users to reach effective MI-BCI control, with studies reporting positive effects of four individual elements on user performance and experience, namely: feedback, avatars, assistance, and social interaction. Based on these elements, this review makes current and future recommendations for effective gamification, such as the use of virtual reality and adaptation of game difficulty to user skill level.

The production of three Higgs bosons at hadron colliders can be enhanced by a double-resonant effect in the $\mathbb{Z}_2$-symmetric two-real-singlet extension of the Standard Model, making it potentially observable in future LHC runs. The production rate is maximized for large scalar couplings, which prompts us to carefully reconsider the perturbativity constraints on the theory. This leads us to construct a new set of 140 benchmark points that have a triple Higgs boson production cross-section at least 100 times larger than the SM value. Furthermore, we study the dynamics of the electroweak phase transition, both analytically at leading order, and numerically without the high-temperature expansion. Both analyses indicate that a first-order phase transition is incompatible with the requirement that both singlets have a non-zero vev in the present-day vacuum, as required by doubly-enhanced triple Higgs boson production. Allowing instead one of the singlets to remain at zero field value opens up the possibility of a first-order phase transition, while di-Higgs boson production can still be enhanced by a (single) resonance., Comment: 33 pages, 5 figures. 140 new benchmark points for the TRSM are included in the ancillary file New_BM_points_arXiv.dat

This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content that has been modified using techniques designed to evade detection by these tools (n=805). The results demonstrate that the detectors' already low accuracy rates (39.5%) show major reductions in accuracy (17.4%) when faced with manipulated content, with some techniques proving more effective than others in evading detection. The accuracy limitations and the potential for false accusations demonstrate that these tools cannot currently be recommended for determining whether violations of academic integrity have occurred, underscoring the challenges educators face in maintaining inclusive and fair assessment practices. However, they may have a role in supporting student learning and maintaining academic integrity when used in a non-punitive manner. These results underscore the need for a combined approach to addressing the challenges posed by GenAI in academia to promote the responsible and equitable use of these emerging technologies. The study concludes that the current limitations of AI text detectors require a critical approach for any possible implementation in HE and highlight possible alternatives to AI assessment strategies.

Previous research has shown that Artificial Intelligence is capable of distinguishing between authentic paintings by a given artist and human-made forgeries with remarkable accuracy, provided sufficient training. However, with the limited amount of existing known forgeries, augmentation methods for forgery detection are highly desirable. In this work, we examine the potential of incorporating synthetic artworks into training datasets to enhance the performance of forgery detection. Our investigation focuses on paintings by Vincent van Gogh, for which we release the first dataset specialized for forgery detection. To reinforce our results, we conduct the same analyses on the artists Amedeo Modigliani and Raphael. We train a classifier to distinguish original artworks from forgeries. For this, we use human-made forgeries and imitations in the style of well-known artists and augment our training sets with images in a similar style generated by Stable Diffusion and StyleGAN. We find that the additional synthetic forgeries consistently improve the detection of human-made forgeries. In addition, we find that, in line with previous research, the inclusion of synthetic forgeries in the training also enables the detection of AI-generated forgeries, especially if created using a similar generator., Comment: 15 + 10 pages, 4 + 5 figures, 4 + 10 tables; van Gogh dataset available, DOI: https://doi.org/10.5281/zenodo.10276928

We present the Physics at High Angular resolution in Nearby GalaxieS (PHANGS)-AstroSat atlas, which contains ultraviolet imaging of 31 nearby star-forming galaxies captured by the Ultraviolet Imaging Telescope (UVIT) on the AstroSat satellite. The atlas provides a homogeneous data set of far- and near-ultraviolet maps of galaxies within a distance of 22 Mpc and a median angular resolution of 1.4 arcseconds (corresponding to a physical scale between 25 and 160 pc). After subtracting a uniform ultraviolet background and accounting for Milky Way extinction, we compare our estimated flux densities to GALEX observations, finding good agreement. We find candidate extended UV disks around the galaxies NGC 6744 and IC 5332. We present the first statistical measurements of the clumping of the UV emission and compare it to the clumping of molecular gas traced with ALMA. We find that bars and spiral arms exhibit the highest degree of clumping, and the molecular gas is even more clumped than the FUV emission in galaxies. We investigate the variation of the ratio of observed FUV to H$\alpha$ in different galactic environments and kpc-sized apertures. We report that $\sim 65 \%$ varation of the $\log_{10}$(FUV/H$\alpha$) can be described through a combination of dust attenuation with star formation history parameters. The PHANGS-AstroSat atlas enhances the multi-wavelength coverage of our sample, offering a detailed perspective on star formation. When integrated with PHANGS data sets from ALMA, VLT-MUSE, HST and JWST, it develops our comprehensive understanding of attenuation curves and dust attenuation in star-forming galaxies., Comment: 35 pages, 16 figures. The survey webpage is available at https://sites.google.com/view/phangs/home/data/astrosat and the data archive can be accessed at https://www.canfar.net/storage/vault/list/phangs/RELEASES/PHANGS-AstroSat/v1p0

Plastic sorting is a very essential step in waste management, especially due to the presence of multilayer plastics. These monomaterial and multimaterial plastics are widely employed to enhance the functional properties of packaging, combining beneficial properties in thickness, mechanical strength, and heat tolerance. However, materials containing multiple polymer species need to be pretreated before they can be recycled as monomaterials and therefore should not end up in monomaterial streams. Industry 4.0 has significantly improved materials sorting of plastic packaging in speed and accuracy compared to manual sorting, specifically through Near Infrared Hyperspectral Imaging (NIRHSI) that provides an automated, fast, and accurate material characterization, without sample preparation. Identification of multimaterials with HSI however requires novel dedicated approaches for chemical pattern recognition. Non negative Matrix Factorization, NMF, is widely used for the chemical resolution of hyperspectral images. Chemically relevant model constraints may make it specifically valuable to identify multilayer plastics through HSI. Specifically, Multi Block Non Negative Matrix Factorization (MBNMF) with correspondence among different chemical species constraint may be used to evaluate the presence or absence of particular polymer species. To translate the MBNMF model into an evidence based sorting decision, we extended the model with an F test to distinguish between monomaterial and multimaterial objects. The benefits of our new approach, MBNMF, were illustrated by the identification of several plastic waste objects.

In recent years, Transformers, initially developed for language, have been successfully applied to visual tasks. Vision Transformers have been shown to push the state-of-the-art in a wide range of tasks, including image classification, object detection, and semantic segmentation. While ample research has shown promising results in art attribution and art authentication tasks using Convolutional Neural Networks, this paper examines if the superiority of Vision Transformers extends to art authentication, improving, thus, the reliability of computer-based authentication of artworks. Using a carefully compiled dataset of authentic paintings by Vincent van Gogh and two contrast datasets, we compare the art authentication performances of Swin Transformers with those of EfficientNet. Using a standard contrast set containing imitations and proxies (works by painters with styles closely related to van Gogh), we find that EfficientNet achieves the best performance overall. With a contrast set that only consists of imitations, we find the Swin Transformer to be superior to EfficientNet by achieving an authentication accuracy of over 85%. These results lead us to conclude that Vision Transformers represent a strong and promising contender in art authentication, particularly in enhancing the computer-based ability to detect artistic imitations., Comment: Accepted for publication in Neural Computing and Applications

We present the first results of a 21 cm HI line pilot observation carried out with MeerKAT in preparation for the ViCTORIA project, an untargeted survey of the Virgo galaxy cluster. The extraordinary quality of the data in terms of sensitivity and angular resolution (rms~0.65 mJy beam^-1 at ~27"x39" and 11 km/s resolution) allowed us to detect an extended (~10 kpc projected length) low column density (N(HI) < 2.5x10^20 cm^-2) HI gas tail associated with the dwarf irregular galaxy NGC4523 at the northern edge of the cluster. The morphology of the tail and of the stellar disc suggest that the galaxy is suffering a hydrodynamic interaction with the surrounding hot intracluster medium (ICM; ram pressure stripping). The orientation of the trailing tail, the gradient in the HI gas column density at the interface between the cold ISM and the hot ICM, the velocity of the galaxy with respect to that of the cluster, and its position indicate that NGC4523 is infalling for the first time into Virgo from the NNW background of the cluster. Using a grid of hydrodynamic simulations we derive the impact parameters with the surrounding ICM, and estimate that the galaxy will be at pericentre (D~500-600 kpc) in ~1 Gyr, where ram pressure stripping will be able to remove most, if not all, of its gas. The galaxy is located on the star formation main sequence when its star formation rate is derived using Halpha images obtained during the VESTIGE survey, suggesting that NGC4523 is only at the beginning of its interaction with the surrounding environment. A few HII regions are detected in the Halpha images within the HI gas tail outside the stellar disc. Their ages, derived by comparing their Halpha, FUV, NUV, and optical colours with the predictions of SED fitting models, are <30 Myr, and suggest that these HII regions have formed within the stripped gas., Comment: Accepted for publication in A&A

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

This study explores the robustness of university assessments against the use of Open AI's Generative Pre-Trained Transformer 4 (GPT-4) generated content and evaluates the ability of academic staff to detect its use when supported by the Turnitin Artificial Intelligence (AI) detection tool. The research involved twenty-two GPT-4 generated submissions being created and included in the assessment process to be marked by fifteen different faculty members. The study reveals that although the detection tool identified 91% of the experimental submissions as containing some AI-generated content, the total detected content was only 54.8%. This suggests that the use of adversarial techniques regarding prompt engineering is an effective method in evading AI detection tools and highlights that improvements to AI detection software are needed. Using the Turnitin AI detect tool, faculty reported 54.5% of the experimental submissions to the academic misconduct process, suggesting the need for increased awareness and training into these tools. Genuine submissions received a mean score of 54.4, whereas AI-generated content scored 52.3, indicating the comparable performance of GPT-4 in real-life situations. Recommendations include adjusting assessment strategies to make them more resistant to the use of AI tools, using AI-inclusive assessment where possible, and providing comprehensive training programs for faculty and students. This research contributes to understanding the relationship between AI-generated content and academic assessment, urging further investigation to preserve academic integrity.