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Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education quality at scale. This challenge disproportionately hurts students from under-served communities, who stand to gain the most from high-quality education and are most likely to be taught by inexperienced educators. We introduce Tutor CoPilot, a novel Human-AI approach that leverages a model of expert thinking to provide expert-like guidance to tutors as they tutor. This study presents the first randomized controlled trial of a Human-AI system in live tutoring, involving 900 tutors and 1,800 K-12 students from historically under-served communities. Following a preregistered analysis plan, we find that students working on mathematics with tutors randomly assigned to have access to Tutor CoPilot are 4 percentage points (p.p.) more likely to master topics (p<0.01). Notably, students of lower-rated tutors experienced the greatest benefit, improving mastery by 9 p.p. relative to the control group. We find that Tutor CoPilot costs only $20 per-tutor annually, based on the tutors' usage during the study. We analyze 550,000+ messages using classifiers to identify pedagogical strategies, and find that tutors with access to Tutor CoPilot are more likely to use strategies that foster student understanding (e.g., asking guiding questions) and less likely to give away the answer to the student, aligning with high-quality teaching practices. Tutor interviews qualitatively highlight how Tutor CoPilot's guidance helps them to respond to student needs, though tutors flag common issues in Tutor CoPilot, such as generating suggestions that are not grade-level appropriate. Altogether, our study of Tutor CoPilot demonstrates how Human-AI systems can scale expertise in real-world domains, bridge gaps in skills and create a future where high-quality education is accessible to all students. [Additional funding provided by Accelerate.]

The metal content of a galaxy's interstellar medium reflects the interplay between different evolutionary processes such as feedback from massive stars and the accretion of gas from the intergalactic medium. Despite the expected abundance of low-luminosity galaxies, the low-mass and low-metallicity regime remains relatively understudied. Since the properties of their interstellar medium resemble those of early galaxies, identifying such objects in the Local Universe is crucial to understand the early stages of galaxy evolution. We used the DR3 catalog of the Southern Photometric Local Universe Survey (S-PLUS) to select low-metallicity dwarf galaxy candidates based on color selection criteria typical of metal-poor, star-forming, low-mass systems. The final sample contains approximately 50 candidates. Spectral energy distribution fitting of the 12 S-PLUS bands reveals that $\sim$ 90\% of the candidates are best fit by models with very low stellar metallicities. We obtained long-slit observations with the Gemini Multi-Object Spectrograph to follow-up a pilot sample and confirm whether these galaxies have low metallicities. We find oxygen abundances in the range $7.35<$ 12 + log(O/H) $< 7.93$ (5\% to 17\% of the solar value), confirming their metal-poor nature. Most targets are outliers in the mass-metallicity relation, i.e. they display a low metal content relative to their observed stellar masses. In some cases, perturbed optical morphologies might give evidence of dwarf-dwarf interactions or mergers. These results suggest that the low oxygen abundances may be associated with an external event causing the accretion of metal-poor gas, which dilutes the oxygen abundance in these systems., Comment: 23 pages, 13 figures, submitted to ApJ

Handling imbalanced data streams in regression tasks presents a significant challenge, as rare instances can appear anywhere in the target distribution rather than being confined to its extreme values. In this paper, we introduce novel data-level sampling strategies, \texttt{HistUS} and \texttt{HistOS}, that utilize histogram-based approaches to dynamically balance data streams. Unlike previous methods based on Chebyshev\textquotesingle s inequality, our proposed techniques identify and handle rare cases across the entire distribution effectively. We demonstrate that \texttt{HistUS} and \texttt{HistOS} outperform traditional methods through extensive experiments on synthetic and real-world datasets, leading to more accurate and robust regression models in streaming environments.

The flow structures beneath waves have received significant attention from both theoretical and numerical perspectives. Most studies on this topic assume a flat bottom, leading to questions about the effects of variable bottom topography. To address this gap, we investigate the flow structures beneath stationary waves with constant vorticity, considering the influence of variable topography. Specifically, we numerically analyze the role of vorticity in the emergence of stagnation points and the pressure distribution within the fluid in two bottom topography scenarios: a bump and a hole. Our numerical approach is based on a variation of the classical Dyachenko, Zakharov, and Kuznetsov conformal mapping technique for free-boundary water wave problems. Our results reveal the existence of saddle points beneath wave crests and center beneath depression solitary waves. Additionally, we observe that the pressure can exhibit distinctive features, such as a global minimum on the bottom boundary -- behavior that is markedly different from the usual flat-bottom case.

A group of newly observed extreme trans-Neptunian objects exhibit unexpected orbital confinement, characterized by the alignment of orbital angular momentum vectors and apsidal lines. It is proposed that an undiscovered giant planet, named Planet-9, exists in the solar system's outer regions and causes this clustering. Initial studies suggested Planet-9 could have a mass of 15 Earth masses. However, such a massive planet strongly interacts with scattered disk objects (SDOs; 50 < a < 1000 au) and influences the orbits of short-period comets, resulting in orbital inclinations inconsistent with observations. This study models the formation and long-term evolution of trans-Neptunian object populations and the Oort cloud during the solar system's dynamical instability, using revised parameters for Planet-9. Simulations assume Planet-9 has a mass of 7.5 Earth masses, an inclination of ~20 degrees, a semi-major axis of ~600 au, and an eccentricity of ~0.3. Results suggest a less massive Planet-9 aligns with observed trans-Neptunian object inclinations and the number of ecliptic comets (D > 10 km). Distant Kuiper belt objects with 40 < q < 100 au and 200 < a < 500 au, particularly with significant inclinations, are more likely to align apsidally with Planet-9, with an anti-aligned-to-aligned ratio of 0.5-0.7. Lower inclination objects (<20 degrees) exhibit significant apsidal anti-alignment, with an anti-aligned-to-aligned ratio of 2-4. These findings offer a new observational direction to refine the search for Planet-9., Comment: Accepted in Icarus

A particle subjected to a fluctuating force originated from its interaction with an external quantum system undergoes quantum Brownian motion. This phenomenon is investigated in detail for the case of a particle confined by a harmonic potential and allowed to interact with a reservoir modeled as a continuum of oscillators, with particular emphasis on transient effects. Assuming a linear coupling between the particle and the reservoir, canonical quantization of the system is implemented, and analytical expressions for the quantum correlations are derived. These correlations are then applied to analyze energy conservation, and the particle Brownian motion is characterized from the perspective of the energy extracted from the reservoir.

Multi-band photometric surveys provide a straightforward way to discover and classify astrophysical objects systematically, enabling the study of a large number of targets at relatively low cost. Here we introduce an alternative approach to select Accreting White Dwarf (AWD) candidates following their spectral energy distribution, entirely supported by the twelve photometric bands of the Southern Photometric Local Universe Survey (S-PLUS). The method was validated with optical spectroscopic follow-up with the Gemini South telescope which unambiguously established ten systems as cataclysmic variables (CVs), alongside Swift X-ray observations of four of them. Among the ten CVs presented here are those that may be low-luminosity intermediate polars or WZ Sge-type dwarf novae with rare outbursts, two subclasses that can be easily missed in time-domain and X-ray surveys, the two methods currently dominating the discovery of new CVs. Our approach based on S-PLUS provides an important, complementary tool to uncover the total population of CVs and the complete set of its subclasses, which is an important step towards a full understanding of close binary evolution, including the origin of magnetic fields in white dwarfs and the physics of accretion. Finally, we highlight the potential of S-PLUS beyond AWDs, serving other surveys in the characterization of their sources., Comment: Accepted for publication in ApJL

Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify H$\alpha$-excess point sources in the Southern Sky, combining photometric data from 12 S-PLUS filters with machine learning to improve classification of H$\alpha$-related phenomena. Aims. Our goal is to classify H$\alpha$-excess point sources by distinguishing Galactic and extragalactic objects, particularly those with redshifted emission lines, and identifying variability phenomena like RR Lyrae stars. Methods. We selected H$\alpha$-excess candidates using the ($r - J0660$) vs. ($r - i$) colour-colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). UMAP for dimensionality reduction and HDBSCAN clustering were used to separate source types. Infrared data was incorporated, and a Random Forest model was trained on clustering results to identify key colour features. New colour-colour diagrams from S-PLUS MS and infrared data offer a preliminary classification. Results. Combining multiwavelength data with machine learning significantly improved H$\alpha$-excess source classification. We identified 6956 sources with excess in the $J0660$ filter. Cross-matching with SIMBAD explored object types, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, QSOs, AGNs, and galaxies. Using S-PLUS colours and machine learning, we separated RR Lyrae stars from other sources. The separation of Galactic and extragalactic sources was clearer, but distinguishing cataclysmic variables from QSOs at certain redshifts remained challenging. Infrared data refined the classification, and the Random Forest model highlighted key colour features for future follow-up spectroscopy., Comment: 23 pages, 20 figures, 3 tables, accepted to be published in A&A

Federated learning collaboratively trains a neural network on a global server, where each local client receives the current global model weights and sends back parameter updates (gradients) based on its local private data. The process of sending these model updates may leak client's private data information. Existing gradient inversion attacks can exploit this vulnerability to recover private training instances from a client's gradient vectors. Recently, researchers have proposed advanced gradient inversion techniques that existing defenses struggle to handle effectively. In this work, we present a novel defense tailored for large neural network models. Our defense capitalizes on the high dimensionality of the model parameters to perturb gradients within a subspace orthogonal to the original gradient. By leveraging cold posteriors over orthogonal subspaces, our defense implements a refined gradient update mechanism. This enables the selection of an optimal gradient that not only safeguards against gradient inversion attacks but also maintains model utility. We conduct comprehensive experiments across three different datasets and evaluate our defense against various state-of-the-art attacks and defenses. Code is available at https://censor-gradient.github.io., Comment: Accepted by 32nd Annual Network and Distributed System Security Symposium (NDSS 2025). Code is available at https://censor-gradient.github.io

Studying the interstellar medium in nearby starbursts is essential for understanding the physical mechanisms driving these objects, thought to resemble young star-forming galaxies. This study aims to analyze the physical properties of the first spatially-resolved multi-wavelength SED of an extragalactic source, spanning six decades in frequency (from near-UV to cm wavelengths) at an angular resolution of 3$^{\prime\prime}$ (51 pc at the distance of NGC,253). We focus on the central molecular zone (CMZ) of NGC,253, which contains giant molecular clouds (GMCs) responsible for half of the galaxy's star formation. We use archival data, spanning optical to centimeter wavelengths, to compute SEDs with the GalaPy and CIGALE codes for validation, and analyze stellar optical spectra with the \textsc{starlight} code. Our results show significant differences between central and external GMCs in terms of stellar and dust masses, star formation rates (SFRs), and bolometric luminosities. We identify the best SFR tracers as radio continuum bands at 33 GHz, radio recombination lines, and the total infrared luminosity (L$_{\rm IR}$; 8-1000$\mu$m), as well as 60$\mu$m IR emission. BPT and WHAN diagrams indicate shock signatures in NGC~253's nuclear region, associating it with AGN/star-forming hybrids, though the AGN fraction is negligible ($\leq$7.5%). Our findings show significant heterogeneity in the CMZ, with central GMCs exhibiting higher densities, SFRs, and dust masses compared to external GMCs. We confirm that certain centimeter photometric bands can reliably estimate global SFR at GMC scales., Comment: Submitted to A&A. 33 pages (20 main text), 20 figures (13 main text)

We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory performance on every individual data point. Since any model that meets the prescribed performance threshold is a valid FL solution, the choice of optimization algorithm and its dynamics play a crucial role in shaping the properties of the resulting solutions. In particular, we study a primal-dual approach which dynamically re-weights the importance of each sample during training. To address the challenge of setting a meaningful threshold in practice, we introduce a relaxation of FL that incorporates slack variables of minimal norm. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance., Comment: Published at AISTATS 2025. Code available at https://github.com/juan43ramirez/feasible-learning

Human Activity Recognition (HAR) is the identification and classification of static and dynamic human activities, which find applicability in domains like healthcare, entertainment, security, and cyber-physical systems. Traditional HAR approaches rely on wearable sensors, vision-based systems, or ambient sensing, each with inherent limitations such as privacy concerns or restricted sensing conditions. Recently, Radio Frequency (RF)-based HAR has emerged, relying on the interaction of RF signals with people to infer activities. Reconfigurable Intelligent Surfaces (RISs) offers significant potential in this domain by enabling dynamic control over the wireless environment, thus enhancing the information extracted from RF signals. We present an Hand Gesture Recognition (HGR) approach that employs our own 6.5 GHz RIS design to manipulate the RF medium in an area of interest. We validate the capability of our RIS to control the medium by characterizing its steering response, and further we gather and publish a dataset for HGR classification for three different hand gestures. By employing two Convolutional Neural Networks (CNNs) models trained on data gathered under random and optimized RIS configuration sequences, we achieved classification accuracies exceeding 90%.

This study presents a finite difference method (FDM) to model the electromagnetic field propagation in eccentric coaxial waveguides filled with lossy uniaxially anisotropic media. The formulation utilizes conformal transformation to map the eccentric circular waveguide into an equivalent concentric one. In the concentric problem, we introduce a novel normalized Helmholtz equation to decouple TM and TE modes, and we solve this non-homogeneous partial differential equation using the finite difference in cylindrical coordinates. The proposed approach was validated against perturbation-based, spectral element-based, and finite-integration-based numerical solutions. The preliminary results show that our solution is superior in computational time. Furthermore, our FDM formulation can be extended with minimal adaptations to model complex media problems, such as metamaterial devices, optical fibers, and geophysical exploration sensors., Comment: This work was presented at the SBMO 2024 - XXI Brazilian Symposium on Microwaves and Optoelectronics. For more information about the conference, please visit https://www.sbmo.org.br/sbmo/2024/home

The integration of environmental education into the school curriculum can be inspiring for students to assimilate scientific concepts and methods presented in the Science subjects in a contextualized and interdisciplinary way. To carry out such integration in the classroom it is important that a link between teacher training courses and practical activities on the subject during the training of the student teachers exist, to provide them conditions for testing hypotheses, pedagogical methodologies and evaluate their feasibility, considering real limitations of the school environment. The Institutional Program of Scholarships for Teaching Initiation (PIBID - Programa Institucional de Bolsas de Inicia\c{c}\~ao \`a Doc\^encia) is a Brazilian government policy developed to improve the training of student teachers which provides an ideal scenario for this. In this work, students from PIBID carried out this proposal through the windrow composting process. This was developed at the school considering the selective separation of waste, the construction of compost bins, the introduction and discussion of scientific concepts and experimentation, by monitoring the process. In addition to the beneficial experience for the training of student teachers, we show that the proposal is promising for developing student's skills such as scientific literacy and protagonism, and also the establishment of a school composting program., Comment: 22 pages, in Portuguese language, 4 figures

In this paper, we apply recent findings from urban scaling theory to evaluate how it could be applied to a one-dimensional archetypal city. Our focus is on how the simplicity of a one-dimensional model can provide intuitive insights that might be obscured in more complex, multidimensional models. With this instructive model, it is possible to visualize that if the population's accessibility decreases more slowly than the increase in distance, an agglomeration effect occurs. This leads to increasing returns to scale when considering interactions between people or economies of scale in the case of amenities that serve the city. These findings highlight a key characteristic of complex systems: interacting parts that together exhibit properties that are not evident when considered individually. The results presented here are derived from both numerical simulations and theoretical analysis. Although one-dimensionality is used here as a simplification, many natural (e.g., Su{\l}oszowa in Poland) and artificial cities (e.g., The Line in Saudi Arabia) worldwide are shaped this way.

The interest in leveraging Artificial Intelligence (AI) for surgical procedures to automate analysis has witnessed a significant surge in recent years. One of the primary tools for recording surgical procedures and conducting subsequent analyses, such as performance assessment, is through videos. However, these operative videos tend to be notably lengthy compared to other fields, spanning from thirty minutes to several hours, which poses a challenge for AI models to effectively learn from them. Despite this challenge, the foreseeable increase in the volume of such videos in the near future necessitates the development and implementation of innovative techniques to tackle this issue effectively. In this article, we propose a novel technique called Kinematics Adaptive Frame Recognition (KAFR) that can efficiently eliminate redundant frames to reduce dataset size and computation time while retaining useful frames to improve accuracy. Specifically, we compute the similarity between consecutive frames by tracking the movement of surgical tools. Our approach follows these steps: i) Tracking phase: a YOLOv8 model is utilized to detect tools presented in the scene, ii) Similarity phase: Similarities between consecutive frames are computed by estimating variation in the spatial positions and velocities of the tools, iii) Classification phase: A X3D CNN is trained to classify segmentation. We evaluate the effectiveness of our approach by analyzing datasets obtained through retrospective reviews of cases at two referral centers. The Gastrojejunostomy (GJ) dataset covers procedures performed between 2017 to 2021, while the Pancreaticojejunostomy (PJ) dataset spans from 2011 to 2022 at the same centers. By adaptively selecting relevant frames, we achieve a tenfold reduction in the number of frames while improving accuracy by 4.32% (from 0.749 to 0.7814)., Comment: 17

Assuming Galactic cosmic rays originate in supernovae and the winds of massive stars, starburst galaxies should produce very-high-energy (VHE; E$>$100 GeV) gamma-ray emission via the interaction of their copious quantities of cosmic rays with the large reservoirs of dense gas within the galaxies. Such VHE emission was detected by VERITAS from the starburst galaxy M 82 in 2008-09. An extensive, multi-year campaign followed these initial observations, yielding a total of 254 h of good quality VERITAS data on M 82. Leveraging modern analysis techniques and the larger exposure, these VERITAS data show a more statistically significant VHE signal ($\sim$6.5 standard deviations ($\sigma$)). The corresponding photon spectrum is well fit by a power law ($\Gamma = 2.3 \pm 0.3_{stat} \pm0.2_{sys}$) and the observed integral flux is F($>$450 GeV) = $(3.2 \pm0.6_{stat} \pm 0.6_{sys}) \times 10^{-13}~\mathrm{cm^{-2}~s}^{-1}$, or $\sim$0.4\% of the Crab Nebula flux above the same energy threshold. The improved VERITAS measurements, when combined with various multi-wavelength data, enable modeling of the underlying emission and transport processes. A purely leptonic scenario is found to be a poor representation of the gamma-ray spectral energy distribution (SED). A lepto-hadronic scenario with cosmic rays following a power-law spectrum in momentum (index $s\simeq 2.25$), and with significant bremsstrahlung below $1$~GeV, provides a good match to the observed SED. The synchrotron emission from the secondary electrons indicates that efficient non-radiative losses of cosmic-ray electrons may be related to advective escape from the starburst core., Comment: 15 pages, 7 figures; Accepted for the publication in The Astrophysical Journal (ApJ)

The performance of the integrated sensing and communication (ISAC) networks is considerably affected by the mobility of the transceiver nodes, user equipment devices (UEs) and the passive objects that are sensed. For instance, the sensing efficiency is considerably affected by the presence or absence of a line-of-sight connection between the sensing transceivers and the object; a condition that may change quickly due to mobility. Moreover, the mobility of the UEs and objects may result in dynamically varying communication-to-sensing and sensing-to communication interference, deteriorating the network performance. In such cases, there may be a need to handover the sensing process to neighbor nodes. In this article, we develop the concept of mobility management in ISAC networks. Here, depending on the mobility of objects and/or the transceiver nodes, the data traffic, the sensing or communication coverage area of the transceivers, and the network interference, the transmission and/or the reception of the sensing signals may be handed over to neighbor nodes. Also, the ISAC configuration and modality - that is, using monostatic or bistatic sensing - are updated accordingly, such that the sensed objects can be continuously sensed with low overhead. We show that mobility management reduces the sensing interruption and boosts the communication and sensing efficiency of ISAC networks.

The existence of singularities in the spectrum of non-Hermitian Hamiltonians leads to a non-trivial spectral topology which can be exploited to generate topological operations. However, their implementation has remained elusive due to the difficulty of generating a true adiabatic evolution. Here, we develop fast, robust control protocols that generate a desired topological operation. Our strategy relies on shortcuts to adiabaticity, but is not a trivial extension. The presence of spectral singularities renders the strategy developed for Hermitian Hamiltonians impractical as it will lead to faulty control protocols. Moreover, due to the dynamics sensitivity to parameter uncertainties, not all shortcuts to adiabaticity can be used in a realistic setting. We illustrate our method in the context of a two-mode non-Hermitian Hamiltonian and discuss why in general celebrated shortcuts to adiabaticiy like transitionless driving and superadiabatic transitionless driving are not appropriate control protocols for non-Hermitian systems., Comment: 17 pages, 12 figures

Topological matter shows promise for applications in quantum information and transport. In this work, we examined the electronic and vibronic properties of a prototype molecular system adsorbed to the edges, domain walls, and the bulk of a Su-Schrieffer Hegger polyacetylene chain in the trivial, metallic, and topological phases. Our results reveal that suitable molecular observables show signatures of the topological phase transition and imply the topological phase has a significant advantage and robustness over the metallic phase for electron donation into the molecular system. This advantage persists in the presence of defects and suggests applications of topological materials in molecular catalysis and sensing., Comment: 12 pages with 10 images

Two-dimensional (2D) carbon-based materials are promising candidates for developing more efficient green energy conversion and storage technologies. This study presents a new 2D carbon allotrope, DOTT-Carbon, characterized by its distinctive and multi-ring structure featuring 12-, 8-, 4-, and 3-membered rings of carbon atoms. We explore its structural, mechanical, and lithium-ion storage properties by employing density functional theory and machine learning simulations. Phonon calculations confirm its structural stability and ab initio molecular dynamics simulations demonstrate its thermal resilience at elevated temperatures. The material exhibits anisotropic mechanical properties, with Young's modulus values varying between 280-330 GPa. DOTT-Carbon displays a lithium-ion storage capacity of 446.28 mAh/g, complemented by a low diffusion barrier (0.2-0.9 eV) and a high diffusion coefficient ($ > 1.0 \times 10^{-6}$ cm$^{2}$/s), possibly facilitating efficient lithium-ion transport. The stable open circuit voltage of 0.28 V also indicates its suitability as an anode material., Comment: 19 pages

Video-based Automatic License Plate Recognition (ALPR) involves extracting vehicle license plate text information from video captures. Traditional systems typically rely heavily on high-end computing resources and utilize multiple frames to recognize license plates, leading to increased computational overhead. In this paper, we propose two methods capable of efficiently extracting exactly one frame per vehicle and recognizing its license plate characters from this single image, thus significantly reducing computational demands. The first method uses Visual Rhythm (VR) to generate time-spatial images from videos, while the second employs Accumulative Line Analysis (ALA), a novel algorithm based on single-line video processing for real-time operation. Both methods leverage YOLO for license plate detection within the frame and a Convolutional Neural Network (CNN) for Optical Character Recognition (OCR) to extract textual information. Experiments on real videos demonstrate that the proposed methods achieve results comparable to traditional frame-by-frame approaches, with processing speeds three times faster., Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 2024

Video-based vehicle detection and counting play a critical role in managing transport infrastructure. Traditional image-based counting methods usually involve two main steps: initial detection and subsequent tracking, which are applied to all video frames, leading to a significant increase in computational complexity. To address this issue, this work presents an alternative and more efficient method for vehicle detection and counting. The proposed approach eliminates the need for a tracking step and focuses solely on detecting vehicles in key video frames, thereby increasing its efficiency. To achieve this, we developed a system that combines YOLO, for vehicle detection, with Visual Rhythm, a way to create time-spatial images that allows us to focus on frames that contain useful information. Additionally, this method can be used for counting in any application involving unidirectional moving targets to be detected and identified. Experimental analysis using real videos shows that the proposed method achieves mean counting accuracy around 99.15% over a set of videos, with a processing speed three times faster than tracking based approaches., Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 2023

Van der Waals heterostructures have promised the realisation of artificial materials with multiple physical phenomena such as giant optical nonlinearities, spin-to-charge interconversion in spintronics and topological carrier protection, in a single layered device through an infinitely diverse set of quantum materials. However, most efforts have only focused on exfoliated material that inherently limits both the dimensions of the materials and the scalability for applications. Here, we show the epitaxial growth of large area heterostructures of topological insulators (Bi2Se3), transition metal dichalcogenides (TMDs, WSe2) and ferromagnets (Co), resulting in the combination of functionalities including tuneable optical nonlinearities, spin-to-charge conversion and magnetic proximity effects. This is demonstrated through coherent phase resolved terahertz currents, bringing novel functionalities beyond those achievable in simple homostructures. In particular, we show the role of different TMD polymorphs, with the simple change of one atomic monolayer of the artificial material stack entirely changing its optical, electrical and magnetic properties. This epitaxial integration of diverse two-dimensional materials offers foundational steps towards diverse perspectives in quantum material engineering, where the material polymorph can be controlled at technological relevant scales for coupling applications in, for example, van der Waals nonlinear optics, optoelectronics, spintronics, multiferroics and coherent current control.

Automatic License Plate Recognition (ALPR) involves extracting vehicle license plate information from image or a video capture. These systems have gained popularity due to the wide availability of low-cost surveillance cameras and advances in Deep Learning. Typically, video-based ALPR systems rely on multiple frames to detect the vehicle and recognize the license plates. Therefore, we propose a system capable of extracting exactly one frame per vehicle and recognizing its license plate characters from this singular image using an Optical Character Recognition (OCR) model. Early experiments show that this methodology is viable., Comment: Accepted to CVPR 2024

Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing \textit{brain age gap} characterized by an elevated brain age relative to the chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. Hence, brain age gap is a promising biomarker for monitoring brain health. However, black-box machine learning approaches to brain age gap prediction have limited practical utility. Recent studies on coVariance neural networks (VNN) have proposed a relatively transparent deep learning pipeline for neuroimaging data analyses, which possesses two key features: (i) inherent \textit{anatomically interpretablity} of derived biomarkers; and (ii) a methodologically interpretable perspective based on \textit{linkage with eigenvectors of anatomic covariance matrix}. In this paper, we apply the VNN-based approach to study brain age gap using cortical thickness features for various prevalent neurodegenerative conditions. Our results reveal distinct anatomic patterns for brain age gap in Alzheimer's disease, frontotemporal dementia, and atypical Parkinsonian disorders. Furthermore, we demonstrate that the distinct anatomic patterns of brain age gap are linked with the differences in how VNN leverages the eigenspectrum of the anatomic covariance matrix, thus lending explainability to the reported results., Comment: Accepted at ISBI, 2025

Inter-city interactions are critical for the transmission of infectious diseases, yet their effects on the scaling of disease cases remain largely underexplored. Here, we use the commuting network as a proxy for inter-city interactions, integrating it with a general scaling framework to describe the incidence of seven infectious diseases across Brazilian cities as a function of population size and the number of commuters. Our models significantly outperform traditional urban scaling approaches, revealing that the relationship between disease cases and a combination of population and commuters varies across diseases and is influenced by both factors. Although most cities exhibit a less-than-proportional increase in disease cases with changes in population and commuters, more-than-proportional responses are also observed across all diseases. Notably, in some small and isolated cities, proportional rises in population and commuters correlate with a reduction in disease cases. These findings suggest that such towns may experience improved health outcomes and socioeconomic conditions as they grow and become more connected. However, as growth and connectivity continue, these gains diminish, eventually giving way to challenges typical of larger urban areas - such as socioeconomic inequality and overcrowding - that facilitate the spread of infectious diseases. Our study underscores the interconnected roles of population size and commuter dynamics in disease incidence while highlighting that changes in population size exert a greater influence on disease cases than variations in the number of commuters., Comment: 13 pages, 6 figures, supplementary information; accepted for publication in Scientific Reports

We review observations of solar activity, geomagnetic variation, and auroral visibility for the extreme geomagnetic storm on 1872 February 4. The extreme storm (referred to here as the Chapman-Silverman storm) apparently originated from a complex active region of moderate area (\approx 500 {\mu}sh) that was favorably situated near disk center (S19{\deg} E05{\deg}). There is circumstantial evidence for an eruption from this region at 9--10 UT on 1872 February 3, based on the location, complexity, and evolution of the region, and on reports of prominence activations, which yields a plausible transit time of \approx29 hr to Earth. Magnetograms show that the storm began with a sudden commencement at \approx14:27 UT and allow a minimum Dst estimate of {\pounds} -834 nT. Overhead aurorae were credibly reported at Jacobabad (British India) and Shanghai (China), both at 19{\deg}.9 in magnetic latitude (MLAT) and 24{\deg}. 2 in invariant latitude (ILAT). Auroral visibility was reported from 13 locations with MLAT below |20|{\deg} for the 1872 storm (ranging from |10{\deg}. 0|--|19{\deg}. 9| MLAT) versus one each for the 1859 storm (|17{\deg}. 3| MLAT) and the 1921 storm (|16.{\deg}2| MLAT). The auroral extension and conservative storm intensity indicate a magnetic storm of comparable strength to the extreme storms of 1859 September (25{\deg}.1 \pm 0{\deg}.5 ILAT and -949 \pm 31 nT) and 1921 May (27{\deg}.1 ILAT and -907 \pm 132 nT), which places the 1872 storm among the three largest magnetic storms yet observed., Comment: 20 pages, 13 figures, 2 tables, published

The global emphasis on sustainable technologies has become a paramount concern for nations worldwide. Specifically, numerous sustainable methods are being explored as promising alternatives to the well-established vapor-compression technologies in cooling and heating devices. One such avenue gaining traction within the scientific community is the elastocaloric effect (eC). This phenomenon holds promise for efficient cooling and heating processes without causing environmental harm. Studies carried out at the nanoscale have demonstrated the efficiency of the eC, proving to be comparable to that of state-of-the-art macroscopic systems. In this study, we used classical molecular dynamics simulations to investigate the elastocaloric effect for {\gamma}-graphyne. Our analysis goes beyond obtaining changes in eC temperature and the coefficient of performance (COP) for two species of {\gamma}-graphyne nanoribbons (armchair and zigzag). We also explore their dependence on various conditions, including whether they are on deposited on a substrate or pre-strained. Our findings reveal a substantial enhancement in the elastocaloric effect for {\gamma}-graphyne nanoribbons when subjected to pre-strain, amplifying it by at least one order of magnitude. Under certain conditions, the change in the eC temperature and the COP of the structures reach expressive values as high as 224 K and 14, respectively. We discuss the implications of these results by examining the shape and behavior of the carbon-carbon bond lengths within the structures., Comment: Version submitted for publication, to satisfy sponsor requirements

SPARC4 is a new astronomical instrument developed entirely by Brazilian institutions, currently installed on the 1.6-m Perkin-Elmer telescope of the Pico dos Dias Observatory. It allows the user to perform photometric or polarimetric observations simultaneously in the four SDSS bands (g, r, i, and z). In this paper, we describe the control system developed for SPARC4. This system is composed of S4ACS, S4ICS, and S4GUI softwares and associated hardware. S4ACS is responsible for controlling the four EMCCD scientific cameras (one for each instrument band). S4ICS controls the sensors and motors responsible for the moving parts of SPARC4. Finally, S4GUI is the interface used to perform observations, which includes the choice of instrument configuration and image acquisition parameters. S4GUI communicates with the instrument subsystems and with some observatory facilities, needed during the observations. Bench tests were performed for the determination of the overheads added by SPARC4 control system in the acquisition of photometric and polarimetric series of images. In the photometric mode, SPARC4 allows the acquisition of a series of 1400 full-frame images, with a deadtime of 4.5 ms between images. Besides, several image series can be concatenated with a deadtime of 450 ms plus the readout time of the last image. For the polarimetric mode, measurements can be obtained with a deadtime of 1.41 s plus the image readout time between subsequent waveplate positions. For both photometric and polarimetric modes, the user can choose among operating modes with image readout times between 5.9 ms and 1.24 s, which ultimately defines the instrument temporal performance., Comment: 20 pages, 14 figures, peer-reviewed paper

We analyze the fate of spiral order in a one-dimensional system of localized magnetic moments coupled to itinerant electrons under a voltage bias. Within an adiabatic approximation for the dynamics of the localized spins, and in the presence of a phenomenological damping term, we demonstrate the occurrence of various dynamical regimes: At small bias a rigidly rotating non-coplanar magnetic structure is realized which, by increasing the applied voltage, transitions to a quasi-periodic and, finally, fully chaotic evolution. These phases can be identified by transport measurements. In particular, the rigidly rotating state results in an average transfer of spin polarization. We analyze in detail the dependence of the rotation axis and frequency on system's parameters and show that the spin dynamics slows down in the thermodynamic limit, when a static conical state persists to arbitrarily long times. Our results suggest the possibility of discovering non-trivial dynamics in other symmetry-broken quantum states under bias., Comment: 12 pages, 13 figures

Two-dimensional (2D) carbon materials have been intensively investigated because of their distinctive structural framework and electronic behaviors as alternatives in energy conversion and storage applications. This study proposes a novel 2D carbon allotrope, Polymerized Pyracyclene Graphyne (PolyPyGY), characterized by a multi-ringed structure with 4-, 5-, 6-, 8-, and 16-membered rings comprising a porous structure. Using first-principles calculations and machine-learning techniques, we explore its structural, electronic, mechanical, optical, and lithium-ion storing properties. The vibrational properties assessed through the density functional perturbation theory framework confirm its structural stability. Moreover, ab initio molecular dynamics simulations at 1000 K demonstrate its thermal resilience, with no bond breaking or reconfiguration observed. The electronic band structure reveals a metallic nature, and the material exhibits anisotropic elastic properties, with Young's modulus varying between 421 and 664 GPa, suggesting good mechanical stability. Furthermore, lithium diffusion studies indicate low energy barriers (0.05-0.9 eV) and a high diffusion coefficient ( $>$ 6 $\times$ 10$^{-6}$ cm$^{2}$/s), along with a stable open circuit voltage of 1.2 V. These results highlight PolyPyGY's potential as a highly effective and durable anode material for lithium-ion batteries, featuring rapid Li-ion diffusion, stable intercalation, and consistent performance during charge and discharge cycles., Comment: 18 pages

The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks such as evidence synthesis and data extraction from a biomedical corpus of documents. While LLMs demonstrate remarkable potential, significant challenges remain, including issues related to hallucinations, contextual understanding, and the ability to generalize across diverse medical tasks. We highlight critical gaps in the current research literature, particularly the need for unified benchmarks to standardize evaluations and ensure reliability in real-world applications. In addition, we propose directions for future research, emphasizing the integration of state-of-the-art techniques such as retrieval-augmented generation (RAG) to enhance LLM performance in evidence synthesis. By addressing these challenges and utilizing the strengths of LLMs, we aim to improve access to medical literature and facilitate meaningful discoveries in healthcare., Comment: 9 pages, 1 table, 1 figure, conference paper

Collective excitations of charged particles under the influence of an electromagnetic field give rise to a rich variety of hybrid light-matter quasiparticles with unique properties. In metals, intraband collective response manifested by negative permittivity leads to plasmon-polaritons with extreme field confinement, wavelength "squeezing", and potentially low propagation losses. In contrast, photons in semiconductors commonly couple to interband collective response in the form of exciton-polaritons, which give rise to completely different polaritonic properties, described by a superposition of the photon and exciton and an anti-crossing of the eigenstates. In this work, we identify the existence of plasmon-like collective excitations originating from the interband excitonic response of biased bilayer and trilayer graphene, in the form of graphene-exciton-polaritons (GEPs). We find that GEPs possess electrically tunable polaritonic properties and discover that such excitations follow a universal dispersion law for all surface polaritons in 2D excitonic systems. Accounting for nonlocal corrections to the excitonic response, we find that the GEPs exhibit confinement factors that can exceed those of graphene plasmons, and with moderate losses that would enable their observation in cryo-SNOM experiments. Furthermore, by electrically tuning the excitons energy into the Reststrahlen band of the surrounding hexagonal-boron-nitride, we demonstrate that strong coupling between the excitons and hyperbolic phonon polaritons can be achieved, and well-described by an electromagnetic transmission line model. These predictions of plasmon-like interband collective excitations in biased graphene systems open up new research avenues for tunable plasmonic phenomena based on excitonic systems, and the ability to control and manipulate such phenomena at the atomic scale.

This paper investigates the origin and orbital evolution of S-stars in the Galactic Center using models of binary disruption and relaxation processes. We focus on explaining the recently discovered "zone of avoidance" in S-star orbital parameters, defined as a region where no S-stars are observed with pericenters $\log(r_p / {\rm AU}) \leq 1.57 + 2.6(1 - e)$ pc. We demonstrate that the observed S-star orbital distributions, including this zone of avoidance and their thermal eccentricity distribution, can be largely explained by continuous disruption of binaries near the central supermassive black hole, followed by orbital relaxation. Our models consider binaries originating from large scales (5--100 pc) and incorporate empirical distributions of binary properties. We simulate close encounters between binaries and the black hole, tracking the remnant stars' orbits. The initially highly eccentric orbits of disrupted binary remnants evolve due to non-resonant and resonant relaxation in the Galactic Center potential. While our results provide insights into the formation mechanism of S-stars, there are limitations, such as uncertainties in the initial binary population and mass-function and simplifications in our relaxation models. Despite these caveats, our study demonstrates the power of using S-star distributions to probe the dynamical history and environment of the central parsec of our Galaxy., Comment: 12 pages, 9 figures + appendices. Submitted to A&A

Antarctic krill (Euphausia superba) are among the most abundant species on our planet and serve as a vital food source for many marine predators in the Southern Ocean. In this paper, we utilise statistical spatio-temporal methods to combine data from various sources and resolutions, aiming to accurately model krill abundance. Our focus lies in fitting the model to a dataset comprising acoustic measurements of krill biomass. To achieve this, we integrate climate covariates obtained from satellite imagery and from drifting surface buoys (also known as drifters). Additionally, we use sparsely collected krill biomass data obtained from net fishing efforts (KRILLBASE) for validation. However, integrating these multiple heterogeneous data sources presents significant modelling challenges, including spatio-temporal misalignment and inflated zeros in the observed data. To address these challenges, we fit a Hurdle-Gamma model to jointly describe the occurrence of zeros and the krill biomass for the non-zero observations, while also accounting for misaligned and heterogeneous data sources, including drifters. Therefore, our work presents a comprehensive framework for analysing and predicting krill abundance in the Southern Ocean, leveraging information from various sources and formats. This is crucial due to the impact of krill fishing, as understanding their distribution is essential for informed management decisions and fishing regulations aimed at protecting the species.

We investigate the stability of scalar perturbations around a magnetized stationary compact object in General Relativity. The considered object is one of the simplest exact solutions of Einstein electrovacuum equations corresponding to a spheroidal body endowed with a dipole magnetic moment. It is effectively constructed by imposing a perfect reflection (mirror) boundary condition on a central region of the Gutsunaev-Manko spacetime. A time-domain analysis of the perturbations reveals a quasinormal phase followed by a power-law decaying tail. Our findings suggest that the exterior region of the magnetized compact object is stable in the entire parameter space. Moreover, the system tends to become generically more stable the stronger the magnetization of the central object is. Such findings can be useful for the qualitative understanding of more realistic astrophysical situations involving highly magnetized sources., Comment: 10 pages, 5 figures. Minor corrections. Final version to appear in the PRD

We consider the task of out-of-distribution (OOD) generalization, where the distribution shift is due to an unobserved confounder ($Z$) affecting both the covariates ($X$) and the labels ($Y$). In this setting, traditional assumptions of covariate and label shift are unsuitable due to the confounding, which introduces heterogeneity in the predictor, i.e., $\hat{Y} = f_Z(X)$. OOD generalization differs from traditional domain adaptation by not assuming access to the covariate distribution ($X^\text{te}$) of the test samples during training. These conditions create a challenging scenario for OOD robustness: (a) $Z^\text{tr}$ is an unobserved confounder during training, (b) $P^\text{te}{Z} \neq P^\text{tr}{Z}$, (c) $X^\text{te}$ is unavailable during training, and (d) the posterior predictive distribution depends on $P^\text{te}(Z)$, i.e., $\hat{Y} = E_{P^\text{te}(Z)}[f_Z(X)]$. In general, accurate predictions are unattainable in this scenario, and existing literature has proposed complex predictors based on identifiability assumptions that require multiple additional variables. Our work investigates a set of identifiability assumptions that tremendously simplify the predictor, whose resulting elegant simplicity outperforms existing approaches., Comment: 24 pages, 3 figures

Effective content moderation in online communities is often a delicate balance between maintaining content quality and fostering user participation. In this paper, we introduce post guidance, a novel approach to community moderation that proactively guides users' contributions using rules that trigger interventions as users draft a post to be submitted. For instance, rules can surface messages to users, prevent post submissions, or flag posted content for review. This uniquely community-specific, proactive, and user-centric approach can increase adherence to rules without imposing additional burdens on moderators. We evaluate a version of Post Guidance implemented on Reddit, which enables the creation of rules based on both post content and account characteristics, via a large randomized experiment, capturing activity from 97,616 posters in 33 subreddits over 63 days. We find that Post Guidance (1) increased the number of ``successful posts'' (posts not removed after 72 hours), (2) decreased moderators' workload in terms of manually-reviewed reports, (3) increased contribution quality, as measured by community engagement, and (4) had no impact on posters' own subsequent activity, within communities adopting the feature. Post Guidance on Reddit was similarly effective for community veterans and newcomers, with greater benefits in communities that used the feature more extensively. Our findings indicate that post guidance represents a transformative approach to content moderation, embodying a paradigm that can be easily adapted to other platforms to improve online communities across the Web., Comment: This paper has been accepted at CSCW 2025. Please cite accordingly

This paper describes use of model checking to verify synchronisation properties of an industrial welding system consisting of a cobot arm and an external turntable. The robots must move synchronously, but sometimes get out of synchronisation, giving rise to unsatisfactory weld qualities in problem areas, such as around corners. These mistakes are costly, since time is lost both in the robotic welding and in manual repairs needed to improve the weld. Verification of the synchronisation properties has shown that they are fulfilled as long as assumptions of correctness made about parts outside the scope of the model hold, indicating limitations in the hardware. These results have indicated the source of the problem, and motivated a re-calibration of the real-life system. This has drastically improved the welding results, and is a demonstration of how formal methods can be useful in an industrial setting., Comment: In Proceedings FMAS2024, arXiv:2411.13215

Cities emerged independently across different world regions and historical periods, raising fundamental questions: How did the first urban settlements develop? What social and spatial conditions enabled their emergence? Are these processes universal or context-dependent? Moreover, what distinguishes cities from other human settlements? This paper investigates the drivers behind the creation of cities through a hybrid approach that integrates urban theory, the biological concept of morphospace (the space of all possible configurations), and archaeological evidence. It explores the transition from sedentary hunter-gatherer communities to urban societies, highlighting fundamental forces converging to produce increasingly complex divisions of labour as a central driver of urbanization. Morphogenesis is conceptualized as a trajectory through morphospace, governed by structure-seeking selection processes that balance density, permeability, and information as critical dimensions. The study highlights the non-ergodic nature of urban morphogenesis, where configurations are progressively selected based on their fitness to support the diversifying interactions between mutually dependent agents. The morphospace framework effectively distinguishes between theoretical spatial configurations, non-urban and proto-urban settlements, and contemporary cities. This analysis supports the proposition that cities emerge and evolve as solutions balancing density, permeability, and informational organization, enabling them to support increasingly complex societal functions., Comment: Version 2 includes 20 pages and 8 figures, with updates that correct typos, enhance Figure 8, and clarify items in the conclusion section

There has been a growing excitement that implicit graph generative models could be used to design or discover new molecules for medicine or material design. Because these molecules have not been discovered, they naturally lie in unexplored or scarcely supported regions of the distribution of known molecules. However, prior evaluation methods for implicit graph generative models have focused on validating statistics computed from the thick support (e.g., mean and variance of a graph property). Therefore, there is a mismatch between the goal of generating novel graphs and the evaluation methods. To address this evaluation gap, we design a novel evaluation method called Vertical Validation (VV) that systematically creates thin support regions during the train-test splitting procedure and then reweights generated samples so that they can be compared to the held-out test data. This procedure can be seen as a generalization of the standard train-test procedure except that the splits are dependent on sample features. We demonstrate that our method can be used to perform model selection if performance on thin support regions is the desired goal. As a side benefit, we also show that our approach can better detect overfitting as exemplified by memorization., Comment: Accepted to UAI 2024

Skyrmions are nano-sized magnetic whirls attractive for spintronic applications due to their innate stability. They can emulate the characteristic behavior of various spintronic and electronic devices such as spin-torque nano-oscillators, artificial neurons and synapses, logic devices, diodes, and ratchets. Here, we show that skyrmions can emulate the physics of an RC circuit, the fundamental electric circuit composed of a resistor and a capacitor, on the nanosecond time scale. The equation of motion of a current-driven skyrmion in a quadratic energy landscape is mathematically equivalent to the differential equation characterizing an RC circuit: the applied current resembles the applied input voltage, and the skyrmion position resembles the output voltage at the capacitor. These predictions are confirmed via micromagnetic simulations. We show that such a skyrmion system reproduces the characteristic exponential voltage decay upon charging and discharging the capacitor under constant input. Furthermore, it mimics the low-pass filter behavior of RC circuits by filtering high-frequencies in periodic input signals. Since RC circuits are mathematically equivalent to the Leaky-Integrate-Fire (LIF) model widely used to describe biological neurons, our device concept can also be regarded as a perfect artificial LIF neuron., Comment: Marie Sk{\l}odowska-Curie Actions, H2020-MSCA-ITN-2020; Project acronym SPEAR; Grant Agreement No. 955671