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A split graph is a graph whose vertex set can be partitioned into a clique and an independent set. A connected graph $G$ is said to be $t$-admissible if admits a spanning tree in which the distance between any two adjacent vertices of $G$ is at most $t$. Given a graph $G$, determining the smallest $t$ for which $G$ is $t$-admissible, i.e., the stretch index of $G$ denoted by $\sigma(G)$, is the goal of the $t$-admissibility problem. Split graphs are $3$-admissible and can be partitioned into three subclasses: split graphs with $\sigma = 1$, $2$ or $3$. In this work we consider such a partition while dealing with the problem of coloring the edges of a split graph. Vizing proved that any graph can have its edges colored with $\Delta$ or $\Delta+1$ colors, and thus can be classified as Class $1$ or Class $2$, respectively. The edge coloring problem is open for split graphs in general. In previous results, we classified split graphs with $\sigma = 2$ and in this paper we classify and provide an algorithm to color the edges of a subclass of split graphs with $\sigma = 3$.

The identification of new white dwarfs (WDs) polluted with heavy elements is important since they provide a valuable tool for inferring chemical properties of putative planetary systems accreting material on their surfaces. The Gaia space mission has provided us with an unprecedented amount of astrometric, photometric, and low resolution (XP) spectroscopic data for millions of newly discovered stellar sources, among them thousands of WDs. In order to find WDs among this data and to identify which ones have metals in their atmospheres, we propose a methodology based on an unsupervised artificial intelligence technique called Self-Organizing Maps (SOM). In our approach a nonlinear high-dimensional dataset is projected on a 2D grid map where similar elements fall into the same neuron. By applying this method, we obtained a clean sample of 66,337 WDs. We performed an automatic spectral classification analysis to them, obtaining 143 bona fide polluted WD candidates not previously classified in the literature. The majority of them are cool WDs and we identify in their XP spectra several metallic lines such as Ca, Mg, Na, Li, and K. The fact that we obtain similar precision metrics than those achieved with recent supervised techniques highlights the power of our unsupervised approach to mine the Gaia archives for hidden treasures to follow-up spectroscopically with higher resolution., Comment: Accepted for publication in The Astrophysical Journal (ApJ). 15 pages, 13 figures

Front-end polarization modulation enables improved polarization measurement stability by modulating the targeted signal above the low-frequency $1/f$ drifts associated with atmospheric and instrumental instabilities and diminishes the impact of instrumental polarization. In this work, we present the design and characterization of a new 60-cm diameter Reflective Half-Wave Plate (RHWP) polarization modulator for the 90 GHz band telescope of the Cosmology Large Angular Scale Surveyor (CLASS) project. The RHWP consists of an array of parallel wires (diameter $50~\mathrm{\mu m}$, $175~\mathrm{\mu m}$ pitch) positioned $0.88~\mathrm{mm}$ from an aluminum mirror. In lab tests, it was confirmed that the wire resonance frequency ($f_\mathrm{res}$) profile is consistent with the target, $139~\mathrm{Hz}

The battle against the spread of misinformation on the Internet is a daunting task faced by modern society. Fake news content is primarily distributed through digital platforms, with websites dedicated to producing and disseminating such content playing a pivotal role in this complex ecosystem. Therefore, these websites are of great interest to misinformation researchers. However, obtaining a comprehensive list of websites labeled as producers and/or spreaders of misinformation can be challenging, particularly in developing countries. In this study, we propose a novel methodology for identifying websites responsible for creating and disseminating misinformation content, which are closely linked to users who share confirmed instances of fake news on social media. We validate our approach on Twitter by examining various execution modes and contexts. Our findings demonstrate the effectiveness of the proposed methodology in identifying misinformation websites, which can aid in gaining a better understanding of this phenomenon and enabling competent entities to tackle the problem in various areas of society., Comment: This is a preprint version of a submitted manuscript on the Brazilian Symposium on Multimedia and the Web (WebMedia)

State-of-the-art deep learning entity linking methods rely on extensive human-labelled data, which is costly to acquire. Current datasets are limited in size, leading to inadequate coverage of biomedical concepts and diminished performance when applied to new data. In this work, we propose to automatically generate data to create large-scale training datasets, which allows the exploration of approaches originally developed for the task of extreme multi-label ranking in the biomedical entity linking task. We propose the hybrid X-Linker pipeline that includes different modules to link disease and chemical entity mentions to concepts in the MEDIC and the CTD-Chemical vocabularies, respectively. X-Linker was evaluated on several biomedical datasets: BC5CDR-Disease, BioRED-Disease, NCBI-Disease, BC5CDR-Chemical, BioRED-Chemical, and NLM-Chem, achieving top-1 accuracies of 0.8307, 0.7969, 0.8271, 0.9511, 0.9248, and 0.7895, respectively. X-Linker demonstrated superior performance in three datasets: BC5CDR-Disease, NCBI-Disease, and BioRED-Chemical. In contrast, SapBERT outperformed X-Linker in the remaining three datasets. Both models rely only on the mention string for their operations. The source code of X-Linker and its associated data are publicly available for performing biomedical entity linking without requiring pre-labelled entities with identifiers from specific knowledge organization systems., Comment: This work has been submitted to the IEEE for possible publication

This paper investigates the usage patterns of Facebook among different demographics in the United States, focusing on the consumption of political information and its variability across age, gender, and ethnicity. Employing a novel data donation model, we developed a tool that allows users to voluntarily share their interactions with public Facebook groups and pages, which we subsequently enrich using CrowdTangle. This approach enabled the collection and analysis of a dataset comprising over 1,200 American users. Our findings indicate that political content consumption on Facebook is relatively low, averaging around 17%, and exhibits significant demographic variations. Additionally, we provide insights into the temporal trends of these interactions. The main contributions of this research include a methodological framework for studying social media usage in a privacy-preserving manner, a comprehensive dataset reflective of current engagement patterns, and descriptive insights that highlight demographic disparities and trends over time. This study enhances our understanding of social media's role in information dissemination and its implications for political engagement, offering a valuable resource for researchers and policymakers in a landscape where direct data access is diminishing.

Recent advancements in Artificial Intelligence (AI), particularly the widespread adoption of Large Language Models (LLMs), have significantly enhanced text analysis capabilities. This technological evolution offers considerable promise for automating the review of scientific papers, a task traditionally managed through peer review by fellow researchers. Despite its critical role in maintaining research quality, the conventional peer-review process is often slow and subject to biases, potentially impeding the swift propagation of scientific knowledge. In this paper, we propose RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem, aimed at assessing the relevance of a paper in relation to a specified prompt, analogous to a "call for papers". To address this, we introduce a novel dataset comprised of 25,164 instances. Each instance contains one prompt and four candidate papers, each varying in relevance to the prompt. The objective is to develop a machine learning (ML) model capable of determining the relevance of each paper and identifying the most pertinent one. We explore various baseline approaches, including traditional ML classifiers like Support Vector Machine (SVM) and advanced language models such as BERT. Preliminary findings indicate that the BERT-based end-to-end classifier surpasses other conventional ML methods in performance. We present this problem as a public challenge to foster engagement and interest in this area of research.

With the increasing use of smartphones, instant messaging platforms turned into important communication tools. According to WhatsApp, more than 100 billion messages are sent each day on the app. Communication on these platforms has allowed individuals to express themselves in other types of media, rather than simple text, including audio, videos, images, and stickers. Particularly, stickers are a new multimedia format that emerged with messaging apps, promoting new forms of interactions among users, especially in the Brazilian context, transcending their role as a mere form of humor to become a key element in political strategy. In this regard, we investigate how stickers are being used, unveiling unique characteristics that these media bring to WhatsApp chats and the political use of this new media format. To achieve that, we collected a large sample of messages from WhatsApp public political discussion groups in Brazil and analyzed the sticker messages shared in this context

Rydberg alkaline earth atoms are promising tools for quantum simulation and metrology. When one of the two valence electrons is promoted to long-lived circular states, the second valence electron can be optically manipulated without significant autoionization. We harness this feature to demonstrate laser slowing of a thermal atomic beam of circular strontium atoms. By driving the main ion core 422 nm wavelength resonance, we observe a velocity reduction of 50 m/s without significant autoionization. We also show that a superposition of circular states undergoes very weak decoherence during the cooling process, up to the scattering of more than thousand photons. This robustness opens new perspectives for quantum simulations over long timescales with circular atoms, while simultaneously cooling their motional state. It makes it possible to mitigate the harmful effects of unavoidable heating due to spin-motion coupling during a quantum simulation., Comment: 9 pages

Improved polarization measurements at frequencies below 70 GHz with degree-level angular resolution are crucial for advancing our understanding of the Galactic synchrotron radiation and the potential polarized anomalous microwave emission and ultimately benefiting the detection of primordial $B$ modes. In this study, we present sensitivity-improved 40 GHz polarization maps obtained by combining the CLASS 40 GHz and WMAP $Q$-band data through a weighted average in the harmonic domain. The decision to include WMAP $Q$-band data stems from similarities in the bandpasses. Leveraging the accurate large-scale measurements from WMAP $Q$ band and the high-sensitivity information from CLASS 40 GHz band at intermediate scales, the noise level at $\ell\in[30, 100]$ is reduced by a factor of $2-3$ in the map space. A pixel domain analysis of the polarized synchrotron spectral index ($\beta_s$) using WMAP $K$ band and the combined maps (mean and 16/84th percentile across the $\beta_s$ map: $-3.08_{-0.20}^{+0.20}$) reveals a stronger preference for spatial variation (PTE for a uniform $\beta_s$ hypothesis smaller than 0.001) than the results obtained using WMAP $K$ and $Ka$ bands ($-3.08_{-0.14}^{+0.14}$). The cross-power spectra of the combined maps follow the same trend as other low-frequency data, and validation through simulations indicates negligible bias introduced by the combination method (sub-percent level in the power spectra). The products of this work are publicly available on $\mathtt{LAMBDA}$., Comment: 19 pages, 16 figures, 1 table, accepted for publication in ApJ

We introduce SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows. The narrative and unstructured nature of clinical notes is a major obstacle for healthcare intelligentization. We address a critical problem of structuring clinical notes into clinical data, according to international interoperability standards. We collect and annotate data for three subtasks, namely, international patient summary, clinical impression and medical encounter. We then supervised fine-tuned a state-of-the-art LLM using public and credentialed clinical data. The training is orchestrated in a way that the target model can first support basic clinical tasks such as abbreviation expansion and temporal information extraction, and then learn to perform more complex downstream clinical tasks. Moreover, we address several modeling challenges in the healthcare context, e.g., extra long context window. Our blind pairwise evaluation shows that SoftTiger outperforms other popular open-source models and GPT-3.5, comparable to Gemini-pro, with a mild gap from GPT-4. We believe that LLMs may become a step-stone towards healthcare digitalization and democratization. Therefore, we publicly release SoftTiger models at scales of 13 billion and 70 billion parameters, as well as datasets and code for our innovative scalable evaluation, hopefully, making a significant contribution to the healthcare industry., Comment: Accepted to AAAI 2024 Spring Symposium on Clinical Foundation Models, Stanford University, Stanford, California