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We present a deep HI survey at L-band conducted with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) over the COSMOS field. This survey is strategically designed to overlap with the MIGHTEE COSMOS field, aiming to combine the sensitivity of the FAST and high-resolution of the MeerKAT. We observed the field with FAST for approximately 11 hours covering $\sim$2 square degrees, and reduced the raw data to HI spectral cubes over the frequency range 1310-1420 MHz. The FAST-HI data reach a median 3$\sigma$ column density of $N_{\rm HI}\sim2\times10^{17}$ cm$^{-2}$ over a 5 km s$^{-1}$ channel width, allowing for studies of the distribution of HI gas in various environments, such as in galaxies, the Circum-Galactic Medium (CGM) and Intergalactic Medium (IGM). We visually searched the spectral cubes for HI sources, and found a total of 80 HI detections, of which 56 have been cross-matched with the MIGHTEE-HI catalogue. With the cross-matched sources, we compare their HI masses and find that the total HI mass fraction in the IGM and CGM surrounding the galaxy pairs is statistically higher than the HI fraction surrounding the isolated galaxies by a difference of 13$\pm$4%, indicating that the CGM and IGM associated with interacting systems are richer in neutral hydrogen compared to those around isolated galaxies in the local Universe. We also describe several FAST-MeerKAT synergy projects, highlighting the full potential of exploiting both single-dish and interferometric observations to study the distribution and evolution of the diffuse HI gas., Comment: 13 pages, 14 figures; Accepted for publication in MNRAS; Minor corrections made at proof stage

In this work, the molecular enhancement factors of the $\mathcal{P,T}$-odd interactions involving the electron electric dipole moment ($W_\mathrm{d}$) and the scalar-pseudoscalar nucleon-electron couplings ($W_\mathrm{s}$) are computed for the ground state of the bimetallic molecules YbCu, YbAg and YbAu. These systems offer a promising venue for creating cold molecules by associating laser cooled atoms. The relativistic coupled-cluster approach is used in the calculations and a thorough uncertainty analysis is performed to give accurate and reliable uncertainties to the obtained values. Furthermore, an in-depth investigation of the different electronic structure effects that determine the magnitude of the calculated enhancement factors is carried out, and two different schemes for computing $W_\mathrm{d}$ are compared. The recommended values for the enhancement factors are $(13.24\pm0.03)\times10^{24}\frac{h\,\text{Hz}}{e\,\text{cm}}$, $(12.15\pm0.08)\times10^{24}\frac{h\,\text{Hz}}{e\,\text{cm}}$ and $(2.13\pm0.28)\times10^{24}\frac{h\,\text{Hz}}{e\,\text{cm}}$ for $W_\mathrm{d}$, and $(-48.36\pm0.18)\;h\,\text{kHz}$, $(-45.51\pm0.43)\;h\,\text{kHz}$ and $(5.31\pm1.80)\;h\,\text{kHz}$ for $W_\mathrm{s}$, for YbCu, YbAg and YbAu, respectively., Comment: 15 pages, 9 figures, 8 tables

Nanometer-sized molecular structures exhibiting mechanical-like switching between discrete states are of great interest for their potential uses in nanotechnology and materials science. Designing such structures and understanding how they can be combined to operate synchronously is a key to creating nanoscale functional units. Notable examples recently discovered using atomistic simulations are pyridine-furan and pyridine-pyrrole nanosprings. When slightly stretched in aqueous or organic solutions, these nanosprings exhibit bistable dynamics akin to Duffing nonlinear oscillators. Based on these findings, we designed a hybrid system consisting of several pyridine furan nanosprings attached to a graphene plate in organic solvent and simulated the molecular dynamics of the construct. Our focus is on how the nanosprings coupled by a graphene plate work together, and whether such a design enables the nanosprings to respond synchronously to random perturbations and weak external stimuli. Molecular dynamics simulations of this specific construct are complemented by a theoretical model of coupled bistable systems to understand how the synchronization depends on coupling of bistable units.

Zero-shot voice conversion aims to transfer the voice of a source speaker to that of a speaker unseen during training, while preserving the content information. Although various methods have been proposed to reconstruct speaker information in generated speech, there is still room for improvement in achieving high similarity between generated and ground truth recordings. Furthermore, zero-shot voice conversion for speech in specific domains, such as whispered, remains an unexplored area. To address this problem, we propose a SpeakerVC model that can effectively perform zero-shot speech conversion in both voiced and whispered domains, while being lightweight and capable of running in streaming mode without significant quality degradation. In addition, we explore methods to improve the quality of speaker identity transfer and demonstrate their effectiveness for a variety of voice conversion systems., Comment: Accepted at INTERSPEECH 2024

Video quality assessment (VQA) is a crucial task in the development of video compression standards, as it directly impacts the viewer experience. This paper presents the results of the Compressed Video Quality Assessment challenge, held in conjunction with the Advances in Image Manipulation (AIM) workshop at ECCV 2024. The challenge aimed to evaluate the performance of VQA methods on a diverse dataset of 459 videos, encoded with 14 codecs of various compression standards (AVC/H.264, HEVC/H.265, AV1, and VVC/H.266) and containing a comprehensive collection of compression artifacts. To measure the methods performance, we employed traditional correlation coefficients between their predictions and subjective scores, which were collected via large-scale crowdsourced pairwise human comparisons. For training purposes, participants were provided with the Compressed Video Quality Assessment Dataset (CVQAD), a previously developed dataset of 1022 videos. Up to 30 participating teams registered for the challenge, while we report the results of 6 teams, which submitted valid final solutions and code for reproducing the results. Moreover, we calculated and present the performance of state-of-the-art VQA methods on the developed dataset, providing a comprehensive benchmark for future research. The dataset, results, and online leaderboard are publicly available at https://challenges.videoprocessing.ai/challenges/compressedvideo-quality-assessment.html.

Recent advances in self-supervised learning enabled novel medical AI models, known as foundation models (FMs) that offer great potential for characterizing health from diverse biomedical data. Continuous glucose monitoring (CGM) provides rich, temporal data on glycemic patterns, but its full potential for predicting broader health outcomes remains underutilized. Here, we present GluFormer, a generative foundation model on biomedical temporal data based on a transformer architecture, and trained on over 10 million CGM measurements from 10,812 non-diabetic individuals. We tokenized the CGM training data and trained GluFormer using next token prediction in a generative, autoregressive manner. We demonstrate that GluFormer generalizes effectively to 15 different external datasets, including 4936 individuals across 5 different geographical regions, 6 different CGM devices, and several metabolic disorders, including normoglycemic, prediabetic, and diabetic populations, as well as those with gestational diabetes and obesity. GluFormer produces embeddings which outperform traditional CGM analysis tools, and achieves high Pearson correlations in predicting clinical parameters such as HbA1c, liver-related parameters, blood lipids, and sleep-related indices. Notably, GluFormer can also predict onset of future health outcomes even 4 years in advance. We also show that CGM embeddings from pre-intervention periods in Randomized Clinical Trials (RCTs) outperform other methods in predicting primary and secondary outcomes. When integrating dietary data into GluFormer, we show that the enhanced model can accurately generate CGM data based only on dietary intake data, simulate outcomes of dietary interventions, and predict individual responses to specific foods. Overall, we show that GluFormer accurately predicts health outcomes which generalize across different populations metabolic conditions.

Optomagnonics provides a promising method of a Joule-loss-free spin control that can be performed at ultrafast timescale. However, the cornerstone of optomagnonics is impossibility to focus the light tighter than a diffraction limit. This constrains minimal size of the optically switched magnetic bits and minimal wavelengths of the optically excited spin waves in optomagnonic devices thus preventing its further progress. Here we propose and experimentally demonstrate a novel method of the selective optical excitation of the short exchange spin waves by using a specially designed magnetophotonic grating. This method is based on the creation of the sign-changing profile of the inverse Faraday effect (IFE) induced in a magnetic film due to excitation of the optical guided TE-mode by a femtosecond laser pulse. The spatial period of the IFE profile is subdiffractive which allows to launch narrow band short spin waves whose wavelengths are around 300 nm as was experimentally demonstrated and potentially can be made down to ~ 100 nm. This opens new horizons for optomagnetic applications that are wide ranging, from logical elements to data processing devices.

HCN molecules serve as an important tracer for chemical evolution of elemental nitrogen in the regions of star and planet formation. This is largely explained by the fact that N atoms and N$_2$ molecules are poorly accessible for the observation in the radio and infrared ranges. In turn, gas-phase HCN can be observed at various stages of star formation including disks arounds young stars, cometary comas and atmospheres of the planetary satellites. Despite the large geography of gas-phase observations, an identification of interstellar HCN ice is still lacking. In this work we present a series of infrared spectroscopic measurements performed at the new ultra-high vacuum cryogenic apparatus aiming to facilitate the search for interstellar HCN ice. A series of high resolution laboratory infrared spectra of HCN molecules embedded in the H$_2$O, H$_2$O:NH$_3$, CO, CO$_2$ and CH$_3$OH ices at 10~K temperature is obtained. These interstellar ice analogues aim to simulate the surroundings of HCN molecules by the main constituents of the icy mantles on the surface of the interstellar grains. In addition, the spectra of HCN molecules embedded in the solid C$_6$H$_6$, C$_5$H$_5$N and C$_6$H$_5$NH$_2$ are obtained to somehow simulate the interaction of HCN molecules with carbonaceous material of the grains rich in polycyclic aromatic hydrocarbons. The acquired laboratory spectroscopic data are compared with the publicly available results of NIRSpec James Webb Space Telescope observations towards quiescent molecular clouds performed by the ICEAge team., Comment: 10 pages, 6 figures, accepted to ApJL

We present the results of an exploratory study on how pairs interact with speech commands and touch gestures on a wall-sized display during a collaborative sensemaking task. Previous work has shown that speech commands, alone or in combination with other input modalities, can support visual data exploration by individuals. However, it is still unknown whether and how speech commands can be used in collaboration, and for what tasks. To answer these questions, we developed a functioning prototype that we used as a technology probe. We conducted an in-depth exploratory study with 10 participant pairs to analyze their interaction choices, the interplay between the input modalities, and their collaboration. While touch was the most used modality, we found that participants preferred speech commands for global operations, used them for distant interaction, and that speech interaction contributed to the awareness of the partner's actions. Furthermore, the likelihood of using speech commands during collaboration was related to the personality trait of agreeableness. Regarding collaboration styles, participants interacted with speech equally often whether they were in loosely or closely coupled collaboration. While the partners stood closer to each other during close collaboration, they did not distance themselves to use speech commands. From our findings, we derive and contribute a set of design considerations for collaborative and multimodal interactive data analysis systems. All supplemental materials are available at https://osf.io/8gpv2, Comment: 11 pages, 6 figures, to appear in IEEE TVCG (VIS 2024); correct figure

We present a systematic review, an empirical study, and a first set of considerations for designing visualizations in motion, derived from a concrete scenario in which these visualizations were used to support a primary task. In practice, when viewers are confronted with embedded visualizations, they often have to focus on a primary task and can only quickly glance at a visualization showing rich, often dynamically updated, information. As such, the visualizations must be designed so as not to distract from the primary task, while at the same time being readable and useful for aiding the primary task. For example, in games, players who are engaged in a battle have to look at their enemies but also read the remaining health of their own game character from the health bar over their character's head. Many trade-offs are possible in the design of embedded visualizations in such dynamic scenarios, which we explore in-depth in this paper with a focus on user experience. We use video games as an example of an application context with a rich existing set of visualizations in motion. We begin our work with a systematic review of in-game visualizations in motion. Next, we conduct an empirical user study to investigate how different embedded visualizations in motion designs impact user experience. We conclude with a set of considerations and trade-offs for designing visualizations in motion more broadly as derived from what we learned about video games. All supplemental materials of this paper are available at https://osf.io/3v8wm/}.

In the field of Image Quality Assessment (IQA), the adversarial robustness of the metrics poses a critical concern. This paper presents a comprehensive benchmarking study of various defense mechanisms in response to the rise in adversarial attacks on IQA. We systematically evaluate 25 defense strategies, including adversarial purification, adversarial training, and certified robustness methods. We applied 14 adversarial attack algorithms of various types in both non-adaptive and adaptive settings and tested these defenses against them. We analyze the differences between defenses and their applicability to IQA tasks, considering that they should preserve IQA scores and image quality. The proposed benchmark aims to guide future developments and accepts submissions of new methods, with the latest results available online: https://videoprocessing.ai/benchmarks/iqa-defenses.html.

The purpose of this paper is two-fold. First, to provide a straightforward proof of the Cauchy's invariants (CIs) from the particle relabeling symmetry of the action functional for rotational barotropic flows, using pure geometric relabeling. Second, to emphasize the central role of CIs in the study of rotational flows. The first goal is achieved by introducing a new variational approach for derivivg invariants from symmetries, which, when applied to the Lagrangian action of rotational flows, leads to a simple and natural proof of CIs. The second goal is clarified by showing that all important conservation theorems of rotational flows, either local or integral, are simple consequences of CIs., Comment: 21 pages, 42 references

To understand the origin of extended disks of low-surface brightness (LSB) galaxies, we studied in detail 4 such systems with large disks seen edge-on. Two of them are edge-on giant LSB galaxies (gLSBGs) recently identified by our team. The edge-on orientation of these systems boosts their surface brightnesses that provided an opportunity to characterize stellar populations spectroscopically and yielded the first such measurements for edge-on gLSBGs. We collected deep images of one galaxy using the 1.4-m Milankovi\'c Telescope which we combined with the archival Subaru Hyper Suprime-Cam and DESI Legacy Surveys data available for the three other systems, and measured the structural parameters of the disks. We acquired deep long-slit spectra with the Russian 6-meter telescope and the 10-m Keck II telescope and estimated stellar population properties in the high- and low-surface brightness regions as well as the gas-phase metallicity distribution. The gas metallicity gradients are shallow to flat in the range between 0 and -0.03 dex per exponential disk scale length, which is consistent with the extrapolation of the gradient -- scale length relation for smaller disk galaxies. Our estimates of stellar velocity dispersion in the LSB disks as well as the relative thickness of the disks indicate the dynamical overheating. Our observations favor mergers as the essential stage in the formation scenario for massive LSB galaxies., Comment: 21 pages, 12 figures, accepted for publication in ApJ

Password authentication is a weak point for security as passwords are easily stolen and a user may ignore the security by using a simple password. Therefore, services increasingly demand a second factor. While this may enhance security, it comes with a lower level of usability and another factor to be forgotten. A smartphone is an important device in daily life. With the growing number of sensors and features in a smartphone, keystroke dynamics may provide an easy-to-use method. In this paper, we introduce requirements for biometric authentication and keystroke dynamics. This results in an evaluation scheme, which is applied to three selected approaches. Based on the comparison, keystroke dynamics and the evaluation scheme are discussed. The obtained results indicate that keystroke dynamics can be used as another authentication method but can be bypassed by stronger adversaries. For further research, a common data set would improve the comparability., Comment: Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP

The European Committee for Future Accelerators (ECFA) Early-Career Researcher (ECR) panel, which represents the interests of the ECR community to ECFA, presents in this document its initiatives and activities in the year 2023. This report summarises the process of the first big turnover in the panel composition at the start of 2023 and reports on the activities of the active working groups - either pursued from before or newly established. The overarching goal of the ECFA-ECR panel is to better understand and support the diverse interests of early-career researchers in the ECFA community and beyond., Comment: Editors: Jan-Hendrik Arling, Cecilia Borca, Armin Ilg, Arnau Morancho Tarda, Holly Pacey, Marko Pesut, Elisabetta Spadaro Norella and Marta Urbaniak. arXiv admin note: substantial text overlap with arXiv:2212.11238

In this paper for any field of characteristic different from 2 we find the largest automorphism group of a smooth cubic surface over this field. Moreover, we prove that for a given field a smooth cubic surface with the largest automorphism group is unique up to isomorphism., Comment: arXiv admin note: text overlap with arXiv:2312.17032

Most real-world systems exhibit a multiscale behaviour that needs to be taken into consideration when fitting the effective dynamics to data sampled at a given scale. In the case of stochastic multiscale systems driven by Brownian motion, it has been shown that in order for the Maximum Likelihood Estimators of the parameters of the limiting dynamics to be consistent, data needs to be subsampled at an appropriate rate. Recent advances in extracting effective dynamics for fractional multiscale systems make the same question relevant in the fractional diffusion setting. We study the problem of parameter estimation of the diffusion coefficient in this context. In particular, we consider the multiscale fractional Ornstein-Uhlenbeck system (fractional kinetic Brownian motion) and we provide convergence results for the Maximum Likelihood Estimator of the diffusion coefficient of the limiting dynamics, using multiscale data. To do so, we derive asymptotic bounds for the spectral norm of the inverse covariance matrix of fractional Gaussian noise., Comment: 34 pages, 5 figures

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer., Comment: 6 pages, 2 figures, accepted at 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

Microstructure.jl is a Julia package designed for probabilistic microstructure imaging using diffusion and combined diffusion-relaxometry MRI techniques. It provides a flexible and extendable framework for compartment models and includes robust and unified estimators for parameter fitting and uncertainty quantification. The package incorporates several established models from the literature, such as the spherical mean technique and soma and neurite density imaging (SANDI), along with their extensions for analyzing combined diffusion and T2 mapping data acquired at multiple echo times. For parameter estimation, it features methods like Markov Chain Monte Carlo (MCMC) sampling and Monte Carlo dropout with neural networks, which provide probabilistic estimates by approximating the posteriors of modelling parameters. Microstructure.jl is applicable to both in vivo and ex vivo imaging data. We are currently testing and optimizing the package and are pleased to share its major functionalities, design, and documentation progress., Comment: 22 pages, 6 figures

Friendship and rapport play an important role in the formation of constructive social interactions, and have been widely studied in educational settings due to their impact on student outcomes. Given the growing interest in automating the analysis of such phenomena through Machine Learning (ML), access to annotated interaction datasets is highly valuable. However, no dataset on dyadic child-child interactions explicitly capturing rapport currently exists. Moreover, despite advances in the automatic analysis of human behaviour, no previous work has addressed the prediction of rapport in child-child dyadic interactions in educational settings. We present UpStory -- the Uppsala Storytelling dataset: a novel dataset of naturalistic dyadic interactions between primary school aged children, with an experimental manipulation of rapport. Pairs of children aged 8-10 participate in a task-oriented activity: designing a story together, while being allowed free movement within the play area. We promote balanced collection of different levels of rapport by using a within-subjects design: self-reported friendships are used to pair each child twice, either minimizing or maximizing pair separation in the friendship network. The dataset contains data for 35 pairs, totalling 3h 40m of audio and video recordings. It includes two video sources covering the play area, as well as separate voice recordings for each child. An anonymized version of the dataset is made publicly available, containing per-frame head pose, body pose, and face features; as well as per-pair information, including the level of rapport. Finally, we provide ML baselines for the prediction of rapport.

Although engagement in self-directed wellness exercises typically declines over time, integrating social support such as coaching can sustain it. However, traditional forms of support are often inaccessible due to the high costs and complex coordination. Large Language Models (LLMs) show promise in providing human-like dialogues that could emulate social support. Yet, in-depth, in situ investigations of LLMs to support behavior change remain underexplored. We conducted two randomized experiments to assess the impact of LLM agents on user engagement with mindfulness exercises. First, a single-session study, involved 502 crowdworkers; second, a three-week study, included 54 participants. We explored two types of LLM agents: one providing information and another facilitating self-reflection. Both agents enhanced users' intentions to practice mindfulness. However, only the information-providing LLM, featuring a friendly persona, significantly improved engagement with the exercises. Our findings suggest that specific LLM agents may bridge the social support gap in digital health interventions., Comment: Under review

Axon diameter and myelin thickness are closely related microstructural tissue properties that affect the conduction velocity of action potentials in the nervous system. Imaging them non-invasively with MRI-based methods is thus valuable for studying brain microstructure and function. However, the relationship between MRI-based axon diameter and myelination measures has not been investigated across the brain, mainly due to methodological limitations in estimating axon diameters. In recent years, studies using ultra-high gradient strength diffusion MRI (dMRI) have demonstrated improved estimation of axon diameter across white-matter (WM) tracts in the human brain, making such investigations feasible. In this study, we aim to investigate relationships between tissue microstructure properties with MRI-based methods and compare the imaging findings to histological evidence from the literature. We collected dMRI with ultra-high gradient strength and multi-echo spin-echo MRI on ex vivo macaque and human brain samples on a preclinical scanner. From these data, we estimated axon diameter, intra-axonal signal fraction, myelin water fraction (MWF) and aggregate g-ratio and investigated their correlations. We found that the microstructural imaging parameters exhibited consistent patterns across WM tracts and species. Overall, the findings suggest that MRI-based axon geometry and myelination measures can provide complementary information about fiber morphology, and the relationships between these measures agree with prior histological evidence., Comment: 33 pages, 8 figures

The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexity and literally infinite diversity of conjugated molecular structures present both, an unprecedented opportunity for technological breakthroughs as well as an unseen optimization challenge. Current models rely on big data which do not exist for specialized research films. However, a hybrid computational and high throughput experimental screening workflow allowed us to train predictive models with as little as 149 molecules. We demonstrate a unique closed-loop workflow combining high throughput synthesis and Bayesian optimization that discovers new hole transporting materials with tailored properties for solar cell applications. A series of high-performance molecules were identified from minimal suggestions, achieving up to 26.23% (certified 25.88%) power conversion efficiency in perovskite solar cells. Our work paves the way for rapid, informed discovery in vast molecular libraries, revolutionizing material selection for complex devices. We believe that our approach can be generalized to other emerging fields and indeed accelerate the development of optoelectronic semiconductor devices in general., Comment: 21 pages, 5 figures

This study delves into gender classification systems, shedding light on the interaction between social stereotypes and algorithmic determinations. Drawing on the "averageness theory," which suggests a relationship between a face's attractiveness and the human ability to ascertain its gender, we explore the potential propagation of human bias into artificial intelligence (AI) systems. Utilising the AI model Stable Diffusion 2.1, we have created a dataset containing various connotations of attractiveness to test whether the correlation between attractiveness and accuracy in gender classification observed in human cognition persists within AI. Our findings indicate that akin to human dynamics, AI systems exhibit variations in gender classification accuracy based on attractiveness, mirroring social prejudices and stereotypes in their algorithmic decisions. This discovery underscores the critical need to consider the impacts of human perceptions on data collection and highlights the necessity for a multidisciplinary and intersectional approach to AI development and AI data training. By incorporating cognitive psychology and feminist legal theory, we examine how data used for AI training can foster gender diversity and fairness under the scope of the AI Act and GDPR, reaffirming how psychological and feminist legal theories can offer valuable insights for ensuring the protection of gender equality and non-discrimination in AI systems., Comment: presented at IAIL 2024 the Imagining the AI Landscape After the AI ACT, in conjunction with HHAI2024, Malm\"o, Sweden, June 10, 2024

Graph-based models have become pivotal in understanding and predicting navigational patterns within complex networks. Building on graph-based models, the paper advances path extrapolation methods to efficiently predict Wikipedia navigation paths. The Wikipedia Central Macedonia (WCM) dataset is sourced from Wikipedia, with a spotlight on the Central Macedonia region, Greece, to initiate path generation. To build WCM, a crawling process is used that simulates human navigation through Wikipedia. Experimentation shows that an extension of the graph neural network GRETEL, which resorts to dual hypergraph transformation, performs better on a dense graph of WCM than on a sparse graph of WCM. Moreover, combining hypergraph features with features extracted from graph edges has proven to enhance the model's effectiveness. A superior model's performance is reported on the WCM dense graph than on the larger Wikispeedia dataset, suggesting that size may not be as influential in predictive accuracy as the quality of connections and feature extraction. The paper fits the track Knowledge Discovery and Machine Learning of the 16th International Conference on Advances in Databases, Knowledge, and Data Applications., Comment: The Sixteenth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2024)

The gravitational wave (GW) signal from binary black hole (BBH) mergers is a promising probe of Population III (Pop III) stars. To fully unleash the power of the GW probe, one important step is to understand the relative importance and features of different BBH evolution channels. We model two channels, isolated binary stellar evolution (IBSE) and nuclear star cluster-dynamical hardening (NSC-DH), in one theoretical framework based on the semi-analytical code A-SLOTH, under various assumptions on Pop III initial mass function (IMF), initial binary statistics and high-$z$ nuclear star clusters (NSCs). The NSC-DH channel contributes $\sim 8-95\%$ of Pop III BBH mergers across cosmic history, with higher contributions achieved by initially wider binary stars, more top-heavy IMFs, and more abundant high-$z$ NSCs. The dimensionless stochastic GW background (SGWB) produced by Pop III BBH mergers has peak values $\Omega^{\rm peak}_{\rm GW}\sim 10^{-11}-8\times 10^{-11}$ around observer-frame frequencies $\nu\sim 10-100\ \rm Hz$. The Pop III contribution can be a non-negligible ($\sim 2-32\%$) component in the total SGWB at $\nu\lesssim 10\ \rm Hz$. The estimated detection rates of Pop III BBH mergers by the Einstein Telescope are $\sim 6-230\ \rm yr^{-1}$ and $\sim 30-1230\ \rm yr^{-1}$ for the NSC-DH and IBSE channels, respectively. Pop III BBH mergers in NSCs are more massive than those from IBSE, so they dominate the Pop III SGWB below $20$ Hz in most cases. Besides, the detection rate of Pop III BBH mergers involving at least one intermediate-mass BH above $100\ \rm M_\odot$ by the Einstein Telescope is $\sim 0.5-200\ \rm yr^{-1}$ in NSCs but remains below $0.1\ \rm yr^{-1}$ for IBSE., Comment: 28 + 6 pages, 25 figures, accepted for publication in MNRAS

Self-portraits captured from a short distance might look unnatural or even unattractive due to heavy distortions making facial features malformed, and ill-placed head poses. In this paper, we propose SUPER, a novel method of eliminating distortions and adjusting head pose in a close-up face crop. We perform 3D GAN inversion for a facial image by optimizing camera parameters and face latent code, which gives a generated image. Besides, we estimate depth from the obtained latent code, create a depth-induced 3D mesh, and render it with updated camera parameters to obtain a warped portrait. Finally, we apply the visibility-based blending so that visible regions are reprojected, and occluded parts are restored with a generative model. Experiments on face undistortion benchmarks and on our self-collected Head Rotation dataset (HeRo), show that SUPER outperforms previous approaches both qualitatively and quantitatively, opening new possibilities for photorealistic selfie editing.

X-ray photon correlation spectroscopy (XPCS) is a powerful tool for the investigation of dynamics covering a broad range of time and length scales. The two-time correlation function (TTC) is commonly used to track non-equilibrium dynamical evolution in XPCS measurements, followed by the extraction of one-time correlations. While the theoretical foundation for the quantitative analysis of TTCs is primarily established for equilibrium systems, where key parameters such as diffusion remain constant, non-equilibrium systems pose a unique challenge. In such systems, different projections ("cuts") of the TTC may lead to divergent results if the underlying fundamental parameters themselves are subject to temporal variations. This article explores widely used approaches for TTC calculations and common methods for extracting relevant information from correlation functions on case studies, particularly in the light of comparing dynamics in equilibrium and non-equilibrium systems.

The mechanisms that govern a previously unexplained hardening effect of a single phase Cu-30wt%Zn {\alpha}-brass after heating have been investigated. After cold-work, the alloy possesses an increased yield strength and hardening rate only when heat treated to temperatures close to 220{^\circ}C, and is otherwise softer. Crystallographic texture and microstructure, explored using electron backscatter diffraction (EBSD), describe the deformation heterogeneity including twin development, as a function of heat treatment. When heated, an increased area fraction of deformation twins is observed, with dimensions reaching a critical size that maximises the resistance to dislocation slip in the parent grains. The effect is shown to dominate over other alloy characteristics including short range order, giving serrated yielding during tensile testing which is mostly eliminated after heating. In-situ X-ray diffraction during tensile testing corroborates these findings; dislocation-related line broadening and lattice strain development between as worked and heated {\alpha}-brass is directly related to the interaction between the dislocations and the population of deformation twins. The experiments unambiguously disprove that other thermally-induced microstructure features contribute to thermal hardening. Specifically, the presence of recrystallised grains or second phases do not play a role. As these heat treatments match annealing conditions subjected to {\alpha}-brass during deformation-related manufacturing processes, the results here are considered critical to understand, predict and exploit, where appropriate, any beneficial process-induced structural behaviour.

Type Ia Supernovae (SNe Ia) are used as reliable cosmic distance indicators and their standardization is necessary for a more accurate measurement of the cosmological parameters of the Universe. However, the Hubble diagram still shows some intrinsic dispersion, potentially influenced by the supernova's environment. In this study, we reproduce the Hubble diagram fit for the Pantheon supernova sample, and also investigate the possibility of introducing various standardization equations for supernovae exploded in early- and late-type galaxies. We analyze 330 Pantheon SNe Ia to study how host galaxy morphology affects their standardization. We find that SNe Ia hosted by early-type galaxies have different standardization parameters compared to those hosted by late-type galaxies. We conclude that correcting supernova luminosity for host galaxy morphology is essential to perform the precise cosmological analysis., Comment: 5 pages, 5 figures, proceedings of "Non-Stable Phenomena in the Universe", Byurakan, Armenia (18-23 September 2023)

We use partial wave unitarity to constrain various bespoke four-point amplitudes. We start by constructing bespoke generalizations of the type I superstring amplitude, which we show satisfy dual resonance and have suitable high-energy limits. By analyzing the behavior of partial wave coefficients for highly massive states, we strictly rule out all bespoke amplitudes with asymptotically non-linear Regge trajectories and place constraints on the first few non-trivial parameters in asymptotically linear cases. Finally, we argue that while a large class of unitary bespoke amplitudes fails to satisfy Regge Sum Rules, there exists a smaller sub-class with a vanishing mass gap that is superpolynomially bounded., Comment: 36 pages, 7 figures

Arkani-Hamed and collaborators have recently shown that scattering amplitudes for colored theories can be expressed as integrals over combinatorial objects simply constructed from surfaces decorated by kinematic data. In this paper we extend the curve integral formalism to theories with colored fermionic matter and present a compact formula for the all-loop, all-genus, all-multiplicity amplitude integrand of a colored Yukawa theory. The curve integral formalism makes certain properties of the amplitudes manifest and repackages non-trivial numerators into a single combinatorial object. We also present an efficient formula for $L$-loop integrated amplitudes in terms of a sum over $2^L$ combinatorial determinants., Comment: 34 pages, 20 figures, version accepted in JHEP

The $\cal{CP}$ structure of the Higgs boson is a fundamental property which has not yet been constrained with high precision. $\cal{CP}$ violation in the Yukawa coupling between the Higgs boson and top quark pair can be probed directly at the Large Hadron Collider by measuring top-quark-associated Higgs production. Multivariate analysis techniques commonly developed so far by the experiments are designed for a specific signal model and, therefore, complicate reinterpretations and statistical combinations. With this motivation in mind, we propose a $\cal{CP}$-sensitive extension of the simplified template cross-section (STXS) framework. Considering multiple Higgs decay channels, we perform an in-depth comparison of $\cal{CP}$-sensitive observables and combinations thereof. Our resulting proposal is to extend the existing binning in the transverse momentum of the Higgs boson $p_{T,H}$ by either the pseudorapidity difference of the two top-quarks $\Delta \eta_{t\bar t}$, or a variable that is based on the top quark momenta, namely $b_2$ or the Collins-Soper angle $|\cos\theta^*|$. We demonstrate that this variable selection provides close to optimal sensitivity to the $\cal{CP}$ mixture in the top Yukawa coupling for an integrated luminosity of $300\mathrm{fb}^{-1}$, by comparing it to the results of a multivariate analysis. Our results also suggest a benefit of the two-dimensional STXS extension at 3000$\mathrm{fb}^{-1}$., Comment: 46 pages, 21 figures

We consider a group synchronization problem with multiple frequencies which involves observing pairwise relative measurements of group elements on multiple frequency channels, corrupted by Gaussian noise. We study the computational phase transition in the problem of detecting whether a structured signal is present in such observations by analyzing low-degree polynomial algorithms. We show that, assuming the low-degree conjecture, in synchronization models over arbitrary finite groups as well as over the circle group $SO(2)$, a simple spectral algorithm is optimal among algorithms of runtime $\exp(\tilde{\Omega}(n^{1/3}))$ for detection from an observation including a constant number of frequencies. Combined with an upper bound for the statistical threshold shown in Perry et al., our results indicate the presence of a statistical-to-computational gap in such models with a sufficiently large number of frequencies.