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Clustering data using prior domain knowledge, starting from a partially labeled set, has recently been widely investigated. Often referred to as semi-supervised clustering, this approach leverages labeled data to enhance clustering accuracy. To maximize algorithm performance, it is crucial to ensure the safety of this prior knowledge. Methods addressing this concern are termed safe semi-supervised clustering (S3C) algorithms. This paper introduces the KNN graph-based safety-aware semi-supervised fuzzy c-means algorithm (K-GBS3FCM), which dynamically assesses neighborhood relationships between labeled and unlabeled data using the K-Nearest Neighbors (KNN) algorithm. This approach aims to optimize the use of labeled data while minimizing the adverse effects of incorrect labels. Additionally, it is proposed a mechanism that adjusts the influence of labeled data on unlabeled ones through regularization parameters and the average safety degree. Experimental results on multiple benchmark datasets demonstrate that the graph-based approach effectively leverages prior knowledge to enhance clustering accuracy. The proposed method was significantly superior in 64% of the 56 test configurations, obtaining higher levels of clustering accuracy when compared to other semi-supervised and traditional unsupervised methods. This research highlights the potential of integrating graph-based approaches, such as KNN, with established techniques to develop advanced clustering algorithms, offering significant applications in fields that rely on both labeled and unlabeled data for more effective clustering., Comment: 10 pages

Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fields like medical, legal, and financial sectors. Those are commonly referred to as low-resource domains, which comprise long-tail entities, due to the scarcity of available data. To address this, data augmentation techniques are increasingly being employed to generate additional training instances from the original dataset. In this study, we evaluate the effectiveness of two prominent text augmentation techniques, Mention Replacement and Contextual Word Replacement, on two widely-used NER models, Bi-LSTM+CRF and BERT. We conduct experiments on four datasets from low-resource domains, and we explore the impact of various combinations of training subset sizes and number of augmented examples. We not only confirm that data augmentation is particularly beneficial for smaller datasets, but we also demonstrate that there is no universally optimal number of augmented examples, i.e., NER practitioners must experiment with different quantities in order to fine-tune their projects., Comment: 21 pages, 2 figures

The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools capable of automating and enhancing qualitative analysis. This study systematically maps the literature on the use of LLMs for qualitative research, exploring their application contexts, configurations, methodologies, and evaluation metrics. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes traditionally requiring extensive human input. However, challenges such as reliance on prompt engineering, occasional inaccuracies, and contextual limitations remain significant barriers. This research highlights opportunities for integrating LLMs with human expertise, improving model robustness, and refining evaluation methodologies. By synthesizing trends and identifying research gaps, this study aims to guide future innovations in the application of LLMs for qualitative analysis., Comment: 8 pages, includes 1 figures and 3 tables. Submitted to the WSESE 2025 ICSE Workshop

The adoption of Large Language Models (LLMs) for code generation in data science offers substantial potential for enhancing tasks such as data manipulation, statistical analysis, and visualization. However, the effectiveness of these models in the data science domain remains underexplored. This paper presents a controlled experiment that empirically assesses the performance of four leading LLM-based AI assistants-Microsoft Copilot (GPT-4 Turbo), ChatGPT (o1-preview), Claude (3.5 Sonnet), and Perplexity Labs (Llama-3.1-70b-instruct)-on a diverse set of data science coding challenges sourced from the Stratacratch platform. Using the Goal-Question-Metric (GQM) approach, we evaluated each model's effectiveness across task types (Analytical, Algorithm, Visualization) and varying difficulty levels. Our findings reveal that all models exceeded a 50% baseline success rate, confirming their capability beyond random chance. Notably, only ChatGPT and Claude achieved success rates significantly above a 60% baseline, though none of the models reached a 70% threshold, indicating limitations in higher standards. ChatGPT demonstrated consistent performance across varying difficulty levels, while Claude's success rate fluctuated with task complexity. Hypothesis testing indicates that task type does not significantly impact success rate overall. For analytical tasks, efficiency analysis shows no significant differences in execution times, though ChatGPT tended to be slower and less predictable despite high success rates. This study provides a structured, empirical evaluation of LLMs in data science, delivering insights that support informed model selection tailored to specific task demands. Our findings establish a framework for future AI assessments, emphasizing the value of rigorous evaluation beyond basic accuracy measures., Comment: 11 pages

We calculate the mass renormalization for massive Dirac-like systems in (2+1)D due to the electron-phonon interaction at finite temperatures within the large-$N$ expansion. Our model combines the low-energy limit of charge carriers in a buckled honeycomb lattice with the low-energy approximation for phonons and the electron-phonon interaction in a two-dimensional material. Hence, we obtain a massive Dirac-like field interacting with a two-component vector field $\mathcal{A}_i$, describing the phonon field. This provides a one-loop electron self-energy at finite temperature and, from this, we calculate the renormalized band gap $m_R$. Thereafter, our effective model is applied for describing the renormalized optical band gap in monolayers of transition metal dichalcogenides (TMDs), such as MoS$_2$, MoSe$_2$, WS$_2$, and WSe$_2$, where a good agreement is found for reasonable values of our ultraviolet cutoff $\Lambda\approx 1$ eV. The main result establishes that, at low temperatures, $m_R$ is almost constant while for higher temperatures it is a decreasing linear function of the temperature $T$. We obtain that $m_R$ decreases about $\approx [0.1,0.2]$ eV whenever we increase the temperature from $\approx 4$ K to $500$ K, which is consistent with recent experimental measurements. We also estimate the temperature at which we should observe the transition to the linear regime and obtain typical values in the interval $\approx[110,150]$ K from the four materials we have considered., Comment: 11 pages, 5 figures

In this work we define a class of injective-type norm on tensor products through the environment of sequence classes. Examples and results on this norm will be presented and the duality is studied in this context. As a byproduct, we present the definition of the associated integral-type bilinear forms and also a tensor characterization for a class of sequence spaces.

In this work, we start by examining a spherically symmetric black hole within the framework of non-commutative geometry and apply a modified Newman-Janis method to obtain a new rotating solution. We then investigate its consequences, focusing on the horizon structure, ergospheres, and the black hole's angular velocity. Following this, a detailed thermodynamic analysis is performed, covering surface gravity, Hawking temperature, entropy, and heat capacity. We also study geodesic motion, with particular emphasis on null geodesics and their associated radial accelerations. Additionally, the photon sphere and the resulting black hole shadows are explored. Finally, we compute the quasinormal modes for scalar perturbations using the 6th-order WKB approximation., Comment: 35 pages, 16 figures and 2 tables

Proxima Cen (GJ 551; dM5.5e) is one of only about a dozen fully convective stars known to have a stellar cycle, and the only one to have long-term X-ray monitoring. A previous analysis found that X-ray and mid-UV observations, particularly two epochs of data from Swift, were consistent with a well sampled 7 yr optical cycle seen in ASAS data, but not convincing by themselves. The present work incorporates several years of new ASAS-SN optical data and an additional five years of Swift XRT and UVOT observations, with Swift observations now spanning 2009 to 2021 and optical coverage from late 2000. X-ray observations by XMM-Newton and Chandra are also included. Analysis of the combined data, which includes modeling and adjustments for stellar contamination in the optical and UV, now reveals clear cyclic behavior in all three wavebands with a period of 8.0 yr. We also show that UV and X-ray intensities are anti-correlated with optical brightness variations caused by the cycle and by rotational modulation, discuss possible indications of two coronal mass ejections, and provide updated results for the previous finding of a simple correlation between X-ray cycle amplitude and Rossby number over a wide range of stellar types and ages., Comment: 21 pages, 11 figures, 5 tables. Accepted by The Astrophysical Journal on 5 Nov 2024

This paper presents near-infrared spectropolarimetric and velocimetric observations of the young planet-hosting T Tauri star PDS 70, collected with SPIRou at the 3.6m Canada-France-Hawaii Telescope from 2020 to 2024. Clear Zeeman signatures from magnetic fields at the surface of PDS 70 are detected in our data set of 40 circularly polarized spectra. Longitudinal fields inferred from Zeeman signatures, ranging from -116 to 176 G, are modulated on a timescale of 3.008$\pm$0.006 d, confirming that this is the rotation period of PDS 70. Applying Zeeman-Doppler imaging to subsets of unpolarized and circularly polarised line profiles, we show that PDS 70 hosts low-contrast brightness spots and a large-scale magnetic field in its photosphere, featuring in particular a dipole component of strength 200-420 G that evolves on a timescale of months. From the broadening of spectral lines, we also infer that PDS 70 hosts a small-scale field of 2.51$\pm$0.12 kG. Radial velocities derived from unpolarized line profiles are rotationally modulated as well, and exhibit additional longer-term chromatic variability, most likely attributable to magnetic activity rather than to a close-in giant planet (with a 3sigma upper limit on its minimum mass of ~4 Mjup at a distance of ~0.2 au). We finally confirm that accretion occurs at the surface of PDS 70, generating modulated red-shifted absorption in the 1083.3-nm He i triplet, and show that the large-scale magnetic field, often strong enough to disrupt the inner accretion disc up to the corotation radius, weakens as the star gets fainter and redder (as in 2022), suggesting that dust from the disc more easily penetrates the stellar magnetosphere in such phases., Comment: MNRAS in press (20 pages, 12 figures, 5 tables)

A quasi-unidimensional one-electron double quantum dot is studied within the framework of Shannon informational entropy. Its confinement potential, which is described by an asymmetric harmonic-gaussian function, consists of two wells separated by a potential barrier, and the asymmetry of the potential in respect to the center of the barrier is parameterized by $x_0$. In this work we employ Shannon informational entropy as a tool to investigate changes in the electronic confinement resulting from the modifications in the degree of asymmetry $x_0$. In particular, we notice that Shannon entropy turns out to be very sensitive to the change from the symmetric to the antisymmetric regime. Moreover, we show that Shannon entropy as a function of $x_0$ for an electronic excited state displays an irregular behavior whenever the state energy is in the vicinity of a local extreme (a maximum or a minimum); connected with this we observe an oscillatory behavior of the energy of the excited states as a function of $x_0$.

In this work, we establish sharp and improved regularity estimates for viscosity solutions of Hardy-H\'{e}non-type equations with possibly singular weights and strong absorption governed by the $\infty$-Laplacian $$ \Delta_{\infty} u(x) = |x|^{\alpha}u_+^m(x) \quad \text{in} \quad B_1, $$ under suitable assumptions on the data. In this setting, we derive an explicit regularity exponent that depends only on universal parameters. Additionally, we prove non-degeneracy properties, providing further geometric insights into the nature of these solutions. Our regularity estimates not only improve but also extend, to some extent, the previously obtained results for zero-obstacle and dead-core problems driven by the $\infty$-Laplacian. As an application of our findings, we also address some Liouville-type results for this class of equations.

Atmospheric pressure plasma jets (APPJs) are versatile devices with numerous applications. This work focuses on APPJs generated at the tip of long, flexible tubes using the jet transfer technique. The plasma source consists of a primary discharge and a secondary discharge forming the plasma jet. Discharge power measurements were carried out in a way that it was possible to separate the contribution of the primary discharge from the total power dissipated by the plasma source. Both power and effective current were analyzed under different operating conditions. The results show that the variation of the primary discharge power is much lower than the power dissipated by the plasma jet. Additionally, the electrical characteristics of the plasma device were analyzed. Notable differences were observed between the negative and positive phases of the discharge, with a more resistive load in the negative one, which suggests that the electrical equivalent circuit model changes according to the voltage polarity., Comment: 27 pages, 23 figures

This article addresses the stabilizability of a perturbed quintic defocusing Schr\"odinger equation in $\mathbb{R}^{3}$ at the $H^1$--energy level, considering the influence of a damping mechanism. More specifically, we establish a profile decomposition for both linear and nonlinear systems and use them to show that, under certain conditions, the sequence of nonlinear solutions can be effectively linearized. Lastly, through microlocal analysis techniques, we prove the local exponential stabilization of the solution to the perturbed Schr\"odinger equation in $\mathbb{R}^{3}$ showing an observability inequality for the solution of the system under consideration, which is the key result of this work., Comment: This version has been submitted with minor revisions and an updated bibliography. Feedback is highly appreciated. 56 pages. arXiv admin note: text overlap with arXiv:1004.2768 by other authors

The Effective Field Theory of Large Scale Structure (EFTofLSS) has found tremendous success as a perturbative framework for the evolution of large scale structure, and it is now routinely used to compare theoretical predictions against cosmological observations. The model for the total matter field includes one nuisance parameter at 1-loop order, the effective sound speed, which can be extracted by matching the EFT to full N-body simulations. In this work we first leverage the Layzer-Irvine cosmic energy equation to show that the equation of state can be exactly computed with knowledge of the fully nonlinear power spectrum. When augmented with separate universe methods, we show one can estimate the effective sound speed. This estimate is in good agreement with simulation results, with errors at the few tens of percent level. We apply our method to investigate the cosmology dependence of the effective sound speed and to shed light on what cosmic structures shape its value., Comment: 17+10 pages, 9 figures. Comments are welcome!

We consider two classes of models for spatiotemporal data: one without covariates and one with covariates. If $\mathcal{T}$ is a partition of time and $\mathcal{I}$ a partition of the studied area into zones and if $\mathcal{C}$ is the set of arrival types, we assume that the process of arrivals for time interval $t \in \mathcal{T}$, zone $i \in \mathcal{I}$, and arrival type $c \in \mathcal{C}$ is Poisson with some intensity $\lambda_{c,i,t}$. We discussed the calibration and implementation of such models in \cite{laspatedpaper, laspatedmanual} with corresponding software LASPATED (Library for the Analysis of SPAtioTEmporal Discrete data) available on GitHub at https://github.com/vguigues/LASPATED. In this paper, we discuss the extension of these models when some of the locations are missing in the historical data. We propose three models to deal with missing locations and implemented them both in Matlab and C++. The corresponding code is available on GitHub as an extension of LASPATED at https://github.com/vguigues/LASPATED/Missing_Data. We tested our implementation using the process of emergency calls to an Emergency Health Service where many calls come with missing locations and show the importance and benefit of using models that consider missing locations, rather than discarding the calls with missing locations for the calibration of statistical models for such calls.

Synthetic aperture radar (SAR) is an efficient and widely used remote sensing tool. However, data extracted from SAR images are contaminated with speckle, which precludes the application of techniques based on the assumption of additive and normally distributed noise. One of the most successful approaches to describing such data is the multiplicative model, where intensities can follow a variety of distributions with positive support. The $\mathcal{G}^0_I$ model is among the most successful ones. Although several estimation methods for the $\mathcal{G}^0_I$ parameters have been proposed, there is no work exploring a regression structure for this model. Such a structure could allow us to infer unobserved values from available ones. In this work, we propose a $\mathcal{G}^0_I$ regression model and use it to describe the influence of intensities from other polarimetric channels. We derive some theoretical properties for the new model: Fisher information matrix, residual measures, and influential tools. Maximum likelihood point and interval estimation methods are proposed and evaluated by Monte Carlo experiments. Results from simulated and actual data show that the new model can be helpful for SAR image analysis., Comment: 29 pages, 6 figures, 3 tables

Visual correspondence is a crucial step in key computer vision tasks, including camera localization, image registration, and structure from motion. The most effective techniques for matching keypoints currently involve using learned sparse or dense matchers, which need pairs of images. These neural networks have a good general understanding of features from both images, but they often struggle to match points from different semantic areas. This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. Therefore, the learned descriptors do not require image pairs at inference time, allowing feature caching and fast matching using similarity search, unlike learned matchers. We present adapted versions of six existing descriptors, with an average increase in performance of 29% in camera localization, with comparable accuracy to existing matchers as LightGlue and LoFTR in two existing benchmarks. Both code and trained models are available at https://www.verlab.dcc.ufmg.br/descriptors/reasoning_accv24, Comment: Accepted in ACCV 2024

We develop a novel framework for Large Scale Structure (LSS) perturbation theory, that solves the Vlasov-Poisson system of equations for the distribution function in full phase-space. This approach relaxes the usual apriori assumptions of negligible velocity dispersion and vorticity underlying the Standard Perturbation Theory (SPT). We apply the new method to rederive the usual SPT kernels up to third order in the perturbative expansion. We also show that a counterterm, identical to the one introduced by standard Effective Field Theory (EFT) methods, naturally arises within our framework. We finish by making a precise connection to EFT techniques, which reveals the necessity of the EFTofLSS to self-consistently model the long-wavelength fluid, and illustrates the importance of having theoretical control over short distance fluctuations., Comment: 22+10 pages, 8 figures. Comments are welcome!

Forensic analysts often use social media imagery and texts to understand important events. A primary challenge is the initial sifting of irrelevant posts. This work introduces an interactive process for training an event-centric, learning-based multimodal classification model that automates sanitization. We propose a method based on Bayesian Graph Neural Networks (BGNNs) and evaluate active learning and pseudo-labeling formulations to reduce the number of posts the analyst must manually annotate. Our results indicate that BGNNs are useful for social-media data sifting for forensics investigations of events of interest, the value of active learning and pseudo-labeling varies based on the setting, and incorporating unlabelled data from other events improves performance., Comment: Accepted in IEEE International Workshop on Information Forensics and Security - WIFS 2024, Rome, Italy

The miniaturisation of transistors is approaching its limits due to challenges in heat management and information transfer speed. To overcome these obstacles, emerging technologies such as spintronics are being developed, which leverage the electron's spin in addition to its charge. Local phenomena at interfaces or structural defects will greatly influence the efficiency of spin-based devices, making the ability to study and control spin wave propagation at the nano- and atomic scales a key challenge. The development of high-spatial-resolution tools to probe spin waves, also called magnons, at relevant lengthscales is thus essential to understand how their properties are affected by such local features. Here, we show the first experimental detection of bulk magnons at the nanoscale using scanning transmission electron microscopy. By employing high-resolution electron energy loss spectroscopy with hybrid-pixel direct electron detectors optimized for low acceleration voltages, we successfully overcome the challenges posed by weak signals and identify magnon excitations in a thin NiO nanocrystal. Advanced inelastic electron scattering simulations corroborate our findings. These results open new avenues for detecting magnons, exploring their dispersions and their modifications arising from nanoscale structural or chemical defects. This marks an important milestone in magnonics and presents exciting opportunities for the future development of spintronic devices.

We consider the massless Rarita-Schwinger (RS) LV QED. In this theory, we introduce the gauge fixing to obtain the propagator for the RS field, and calculate the Carroll-Field-Jackiw term, which turns out to be finite and ambiguous, and only in one calculation scheme, based on the nonlinear gauge framework, the gauge independence of the result is achieved., Comment: 15 pages

We propose a method to measure the similarity of papers and authors by simulating a literature search procedure on citation networks, which is an information retrieval inspired conceptualization of similarity. This transition probability (TP) based approach does not require a curated classification system, avoids clustering complications, and provides a continuous measure of similarity. We perform testing scenarios to explore several versions of the general TP concept and the Node2vec machine-learning technique. We found that TP measures outperform Node2vec in mapping the macroscopic structure of fields. The paper provides a general discussion of how to implement TP similarity measurement, with a particular focus on how to utilize publication-level information to approximate the research interest similarity of individual scientists. This paper is accompanied by a Python package capable of calculating all the tested metrics.

Background: Manual testing is vital for detecting issues missed by automated tests, but specifying accurate verifications is challenging. Aims: This study aims to explore the use of Large Language Models (LLMs) to produce verifications for manual tests. Method: We conducted two independent and complementary exploratory studies. The first study involved using 2 closed-source and 6 open-source LLMs to generate verifications for manual test steps and evaluate their similarity to original verifications. The second study involved recruiting software testing professionals to assess their perception and agreement with the generated verifications compared to the original ones. Results: The open-source models Mistral-7B and Phi-3-mini-4k demonstrated effectiveness and consistency comparable to closed-source models like Gemini-1.5-flash and GPT-3.5-turbo in generating manual test verifications. However, the agreement level among professional testers was slightly above 40%, indicating both promise and room for improvement. While some LLM-generated verifications were considered better than the originals, there were also concerns about AI hallucinations, where verifications significantly deviated from expectations. Conclusion: We contributed by generating a dataset of 37,040 test verifications using 8 different LLMs. Although the models show potential, the relatively modest 40% agreement level highlights the need for further refinement. Enhancing the accuracy, relevance, and clarity of the generated verifications is crucial to ensure greater reliability in real-world testing scenarios., Comment: 9 pages

The operation of an ambulance fleet involves ambulance selection decisions about which ambulance to dispatch to each emergency, and ambulance reassignment decisions about what each ambulance should do after it has finished the service associated with an emergency. For ambulance selection decisions, we propose four new heuristics: the Best Myopic (BM) heuristic, a NonMyopic (NM) heuristic, and two greedy heuristics (GHP1 and GHP2). Two variants of the greedy heuristics are also considered. We also propose an optimization problem for an extension of the BM heuristic, useful when a call for several patients arrives. For ambulance reassignment decisions, we propose several strategies to choose which emergency in queue to send an ambulance to or which ambulance station to send an ambulance to when it finishes service. These heuristics are also used in a rollout approach: each time a new decision has to be made (when a call arrives or when an ambulance finishes service), a two-stage stochastic program is solved. The proposed heuristics are used to efficiently compute the second stage cost of these problems. We apply the rollout approach with our heuristics to data of the Emergency Medical Service (EMS) of a large city, and show that these methods outperform other heuristics that have been proposed for ambulance dispatch decisions. We also show that better response times can be obtained using the rollout approach instead of using the heuristics without rollout. Moreover, each decision is computed in a few seconds, which allows these methods to be used for the real-time management of a fleet of ambulances.

This paper describes an online tool for the visualization of medical emergency locations, randomly generated sample paths of medical emergencies, and the animation of ambulance movements under the control of various dispatch methods in response to these emergencies. The tool incorporates statistical models for forecasting emergency locations and call arrival times, the simulation of emergency arrivals and ambulance movement trajectories, and the computation and visualization of performance metrics such as ambulance response time distributions. Data for the Rio de Janeiro Emergency Medical Service are available on the website. A user can upload emergency data for any Emergency Medical Service, and can then use the visualization tool to explore the uploaded data. A user can also use the statistical tools and/or the simulation tool with any of the dispatch methods provided, and can then use the visualization tool to explore the computational output. Future enhancements include the ability of a user to embed additional dispatch algorithms into the simulation; the tool can then be used to visualize the simulation results obtained with the newly embedded algorithms.

Control barrier functions (CBFs) offer an efficient framework for designing real-time safe controllers. However, CBF-based controllers can be short-sighted, resulting in poor performance, a behaviour which is aggravated in uncertain conditions. This motivated research on safety filters based on model predictive control (MPC) and its stochastic variant. MPC deals with safety constraints in a direct manner, however, its computational demands grow with the prediction horizon length. We propose a safety formulation that solves a finite horizon optimization problem at each time instance like MPC, but rather than explicitly imposing constraints along the prediction horizon, we enforce probabilistic safety constraints by means of CBFs only at the first step of the horizon. The probabilistic CBF constraints are transformed in a finite number of deterministic CBF constraints via the scenario based methodology. Capitalizing on results on scenario based MPC, we provide distribution-free, \emph{a priori} guarantees on the system's closed loop expected safety violation frequency. We demonstrate our results through a case study on unmanned aerial vehicle collision-free position swapping, and provide a numerical comparison with recent stochastic CBF formulations., Comment: To be published in: The 63rd IEEE Conference on Decision and Control (CDC-2024 Milano, Italy)

Mode-locked lasers are of interest for applications such as biological imaging, non-linear frequency conversion, and single-photon generation. In the infrared, chip-integrated mode-locked lasers have been demonstrated through integration of laser diodes with low-loss photonic circuits. However additional challenges, such as a higher propagation loss and smaller alignment tolerances have prevented the realization of such lasers in the visible range. Here, we demonstrate the first chip-integrated mode-locked diode laser in the visible using an integrated photonic circuit for cavity extension. Based on a gallium arsenide gain chip and a low-loss silicon nitride feedback circuit, the laser is passively mode-locked using a saturable absorber implemented by focused ion beam milling. At a center wavelength of 642 nm, the laser shows an average output power of 3.4 mW, with a spectral bandwidth of 1.5 nm at a repetition rate of 7.84 GHz., Comment: 7 pages, 4 figures

Platform-based laborers face unprecedented challenges and working conditions that result from algorithmic opacity, insufficient data transparency, and unclear policies and regulations. The CSCW and HCI communities increasingly turn to worker data collectives as a means to advance related policy and regulation, hold platforms accountable for data transparency and disclosure, and empower the collective worker voice. However, fundamental questions remain for designing, governing and sustaining such data infrastructures. In this workshop, we leverage frameworks such as data feminism to design sustainable and power-aware data collectives that tackle challenges present in various types of online labor platforms (e.g., ridesharing, freelancing, crowdwork, carework). While data collectives aim to support worker collectives and complement relevant policy initiatives, the goal of this workshop is to encourage their designers to consider topics of governance, privacy, trust, and transparency. In this one-day session, we convene research and advocacy community members to reflect on critical platform work issues (e.g., worker surveillance, discrimination, wage theft, insufficient platform accountability) as well as to collaborate on codesigning data collectives that ethically and equitably address these concerns by supporting working collectivism and informing policy development.

Computer Graphics (CG) advancements have allowed the creation of more realistic Virtual Humans (VH) through modern techniques for animating the VH body and face, thereby affecting perception. From traditional methods, including blend shapes, to driven animations using facial and body tracking, these advancements can potentially enhance the perception of comfort and realism in relation to VHs. Previously, Psychology studied facial movements in humans, with some works separating expressions into macro and micro expressions. Also, some previous CG studies have analyzed how macro and micro expressions are perceived, replicating psychology studies in VHs, encompassing studies with realistic and cartoon VHs, and exploring different VH technologies. However, instead of using facial tracking animation methods, these previous studies animated the VHs using blendshapes interpolation. To understand how the facial tracking technique alters the perception of VHs, this paper extends the study to macro and micro expressions, employing two datasets to transfer real facial expressions to VHs and analyze how their expressions are perceived. Our findings suggest that transferring facial expressions from real actors to VHs significantly diminishes the accuracy of emotion perception compared to VH facial animations created by artists.

Translating written sentences from oral languages to a sequence of manual and non-manual gestures plays a crucial role in building a more inclusive society for deaf and hard-of-hearing people. Facial expressions (non-manual), in particular, are responsible for encoding the grammar of the sentence to be spoken, applying punctuation, pronouns, or emphasizing signs. These non-manual gestures are closely related to the semantics of the sentence being spoken and also to the utterance of the speaker's emotions. However, most Sign Language Production (SLP) approaches are centered on synthesizing manual gestures and do not focus on modeling the speakers expression. This paper introduces a new method focused in synthesizing facial expressions for sign language. Our goal is to improve sign language production by integrating sentiment information in facial expression generation. The approach leverages a sentence sentiment and semantic features to sample from a meaningful representation space, integrating the bias of the non-manual components into the sign language production process. To evaluate our method, we extend the Frechet Gesture Distance (FGD) and propose a new metric called Frechet Expression Distance (FED) and apply an extensive set of metrics to assess the quality of specific regions of the face. The experimental results showed that our method achieved state of the art, being superior to the competitors on How2Sign and PHOENIX14T datasets. Moreover, our architecture is based on a carefully designed graph pyramid that makes it simpler, easier to train, and capable of leveraging emotions to produce facial expressions.

Despite significant advancements in License Plate Recognition (LPR) through deep learning, most improvements rely on high-resolution images with clear characters. This scenario does not reflect real-world conditions where traffic surveillance often captures low-resolution and blurry images. Under these conditions, characters tend to blend with the background or neighboring characters, making accurate LPR challenging. To address this issue, we introduce a novel loss function, Layout and Character Oriented Focal Loss (LCOFL), which considers factors such as resolution, texture, and structural details, as well as the performance of the LPR task itself. We enhance character feature learning using deformable convolutions and shared weights in an attention module and employ a GAN-based training approach with an Optical Character Recognition (OCR) model as the discriminator to guide the super-resolution process. Our experimental results show significant improvements in character reconstruction quality, outperforming two state-of-the-art methods in both quantitative and qualitative measures. Our code is publicly available at https://github.com/valfride/lpsr-lacd, Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 2024

Vehicle information recognition is crucial in various practical domains, particularly in criminal investigations. Vehicle Color Recognition (VCR) has garnered significant research interest because color is a visually distinguishable attribute of vehicles and is less affected by partial occlusion and changes in viewpoint. Despite the success of existing methods for this task, the relatively low complexity of the datasets used in the literature has been largely overlooked. This research addresses this gap by compiling a new dataset representing a more challenging VCR scenario. The images - sourced from six license plate recognition datasets - are categorized into eleven colors, and their annotations were validated using official vehicle registration information. We evaluate the performance of four deep learning models on a widely adopted dataset and our proposed dataset to establish a benchmark. The results demonstrate that our dataset poses greater difficulty for the tested models and highlights scenarios that require further exploration in VCR. Remarkably, nighttime scenes account for a significant portion of the errors made by the best-performing model. This research provides a foundation for future studies on VCR, while also offering valuable insights for the field of fine-grained vehicle classification., Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 2024

Harnessing human movements to command an Unmanned Aerial Vehicle (UAV) holds the potential to revolutionize their deployment, rendering it more intuitive and user-centric. In this research, we introduce a novel methodology adept at classifying three-dimensional human actions, leveraging them to coordinate on-field with a UAV. Utilizing a stereo camera, we derive both RGB and depth data, subsequently extracting three-dimensional human poses from the continuous video feed. This data is then processed through our proposed k-nearest neighbour classifier, the results of which dictate the behaviour of the UAV. It also includes mechanisms ensuring the robot perpetually maintains the human within its visual purview, adeptly tracking user movements. We subjected our approach to rigorous testing involving multiple tests with real robots. The ensuing results, coupled with comprehensive analysis, underscore the efficacy and inherent advantages of our proposed methodology., Comment: Accepted in International Conference on Informatics in Control, Automation and Robotics (ICINCO) 2024

In many applications, synchronizing audio with visuals is crucial, such as in creating graphic animations for films or games, translating movie audio into different languages, and developing metaverse applications. This review explores various methodologies for achieving realistic facial animations from audio inputs, highlighting generative and adaptive models. Addressing challenges like model training costs, dataset availability, and silent moment distributions in audio data, it presents innovative solutions to enhance performance and realism. The research also introduces a new taxonomy to categorize audio-visual synchronization methods based on logistical aspects, advancing the capabilities of virtual assistants, gaming, and interactive digital media.

This is the User Manual of LASPATED library. This library is available on GitHub (at https://github.com/vguigues/LASPATED)) and provides a set of tools to analyze spatiotemporal data. A video tutorial for this library is available on Youtube. It is made of a Python package for time and space discretizations and of two packages (one in Matlab and one in C++) implementing the calibration of the probabilistic models for stochastic spatio-temporal data proposed in the companion paper arXiv:2203.16371v2., Comment: 25 pages, 6 figures

X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating comprehensive clinical features and data integration. We present a novel framework to enhance the clinical context through augmentation techniques with clinical tabular data, thereby improving its applicability and reliability in AI medical diagnostics. We introduce a pioneering approach to clinical data augmentation that employs large language models to generate patient contextual synthetic data. This methodology is crucial for training more robust deep learning models in healthcare. It preserves the integrity of real patient data while enriching the dataset with contextually relevant synthetic features, significantly enhancing model performance. Our methodology, termed DALL-M, uses a three-phase feature generation process: (i)clinical context storage, (ii)expert query generation, and (iii)context-aware feature augmentation. DALL-M generates new, clinically relevant features by synthesizing chest X-ray images and reports. Applied to 799 cases using nine features from the MIMIC-IV dataset, it created an augmented set of 91 features. This is the first work to generate contextual values for patients' X-ray reports. Specifically, we provide (i)the capacity of LLMs to generate contextual synthetic values for existing clinical features and (ii)their ability to create entirely new clinically relevant features. Empirical validation with machine learning models showed significant performance improvements. Incorporating augmented features increased the F1 score by 16.5% and Precision and Recall by approximately 25%. DALL-M addresses a critical gap in clinical data augmentation, offering a robust framework for generating contextually enriched datasets., Comment: we introduce a pioneering approach to clinical data augmentation that employs large language models (LLMs) to generate patient contextual synthetic data. It preserves the integrity of real patient data while enriching the dataset with contextually relevant synthetic features, significantly enhancing model performance

The potential of tetra-penta-deca-hexagonal graphene (TPDH-gr), a recently proposed 2D carbon allotrope as an anodic material in lithium ion batteries (LIB), was investigated through density functional theory (DFT) calculations. The results indicate that Li-atom adsorption is moderate (around 0.70 eV), allowing for easy desorption. Moreover, energy barrier, diffusion coefficient, and open circuit voltage (OCV) calculations show rapid Li atom diffusion on the TPDH-gr surface, stable intercalation of lithium atoms, and good performance during the charge and discharge cycles of the LIB. These findings, combined with the intrinsic metallic nature of TPDH-gr, indicate that this new 2D carbon allotrope is a promising candidate for use as an anodic LIB material., Comment: Submitted to JPCC

The thermodynamics of quantum phase transitions has long been a rich area of research, providing numerous insights and enhancing our understanding of this important phenomenon. This theoretical framework has been well-developed specially because quantum phase transitions occur at equilibrium. However, its dynamical counterpart, known as dynamical quantum phase transitions (DQPTs), takes place out-of-equilibrium, where conventional thermodynamic tools are inapplicable. In this work, we make progress in this area by connecting dynamical criticality to the second law of thermodynamics through a geometric perspective on entropy production. Our findings, along with other recent developments, suggest that dynamical criticality can lead the system to highly complex dynamics, indicating a possible pathway to thermalization., Comment: 8 pages, 8 figures

We formulate a new class of modified gravity models, that is, generalized four-dimensional Chern-Pontryagin models, whose action is characterized by an arbitrary function of the Ricci scalar $R$ and the Chern-Pontryagin topological term ${}^*RR$, i.e., $f(R, {}^*RR)$. Within this framework, we derive the gravitational field equations and solve them for a particular model, $f(R, {}^*RR)=R+\beta ({}^*RR)^2$, considering two ansatzes: the slowly rotating metric and first-order perturbations of G\"{o}del-type metrics. For the former, we find a first-order correction to the frame-dragging effect boosted by the parameter $L$, which characterizes the departures from general relativity results. For the latter, G\"{o}del-type metrics hold unperturbed. We conclude this paper by displaying that generalized four-dimensional Chern-Pontryagin models admit a scalar-tensor representation, whose explicit form presents two scalar fields: $\Phi$, a dynamical degree of freedom, while the second, $\vartheta$, a non-dynamical degree of freedom. In particular, the scalar field $\vartheta$ emerges coupled with the Chern-Pontryagin topological term ${}^*RR$, i.e., $\vartheta {}^*RR$, which is nothing more than Chern-Simons term., Comment: 23 pages, version accepted to EPJ C

Small business owners (SBOs), specially women, face several challenges in everyday life, especially when asking for microcredit loans from financial institutions. Usual difficulties include low credit scores, unbaked situations, outstanding debts, informal employment situations, inability to showcase their payable capacity, and lack of financial guarantor. Moreover, SBOs often need help applying for microcredit loans due to the lack of information on how to proceed. The task of asking for a loan is a complex practice, and asymmetric power relationships might emerge, but that benefits micro-entrepreneurs only sometimes. In this paper, we interviewed 20 women entrepreneurs living in a low-income community in Brazil. We wanted to unveil value tensions derived from this practice that might influence the design of AI technologies for the public. In doing so, we used a conversational system as a probe to understand the opportunities for empowering their practices with the support of AI multimedia conversational systems. We derived seven recommendations for designing AI systems for evaluating micro-business health in low-income communities.

The recent success of Large Language Models (LLMs) has sparked concerns about their potential to spread misinformation. As a result, there is a pressing need for tools to identify ``fake arguments'' generated by such models. To create these tools, examples of texts generated by LLMs are needed. This paper introduces a methodology to obtain good, bad and ugly arguments from argumentative essays produced by ChatGPT, OpenAI's LLM. We then describe a novel dataset containing a set of diverse arguments, ArGPT. We assess the effectiveness of our dataset and establish baselines for several argumentation-related tasks. Finally, we show that the artificially generated data relates well to human argumentation and thus is useful as a tool to train and test systems for the defined tasks.

We obtain explicit expressions for the class in the Grothendieck group of varieties of the moduli space of genus 0 stable curves with n marked points. This information is equivalent to the Poincar\'e polynomial; it implies explicit expressions for the Betti numbers of the moduli space in terms of Stirling numbers or, alternatively, Bernoulli numbers. The expressions are obtained by solving a differential equation characterizing the generating function for the Grothendieck class as shown in work of Yuri Manin from the 1990s. This differential equation is equivalent to S. Keel's recursion for the Betti numbers of these moduli spaces. Our proof reduces the solution to two combinatorial identities which follow from applications of Lagrange series. We also study generating functions for the individual Betti numbers. In previous work it had been shown that these functions are determined by a set of polynomials with positive rational coefficients, which are conjecturally log-concave. We verify this conjecture for many infinite families of these polynomials, corresponding to the generating functions for the $2k$-Betti numbers of the moduli spaces for all $k\le 100$. Further, studying these polynomials allows us to prove that the generating function for the Grothendieck class of the moduli spaces may be written as a series of rational functions in the Lefschetz motive and the principal branch of the Lambert W-function. We include an interpretation of the main result in terms of Stirling matrices and a discussion of the Euler characteristic of the moduli space., Comment: 31 pages, 1 figure. Substantially expanded version

In this paper, we study the one-loop effective potential of a derivative four-fermion model. As a result, an exact bumblebee-like potential is radiatively generated. Afterwards, we generalize our study for a finite temperature case and explicitly demonstrate the possibility of phase transitions allowing for the restoration of the Lorentz symmetry. We also investigate the low-energy effective action, from which we obtain the usual kinetic term and the corresponding bumblebee potential., Comment: 14 pages, version accepted to EPJ C