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Understanding the prevalence, disparities, and symptom variations of Post COVID-19 Condition (PCC) for vulnerable populations is crucial to improving care and addressing intersecting inequities. This study aims to develop a comprehensive framework for integrating social determinants of health (SDOH) into PCC research by leveraging NLP techniques to analyze disparities and variations in SDOH representation within PCC case reports. Following construction of a PCC Case Report Corpus, comprising over 7,000 case reports from the LitCOVID repository, a subset of 709 reports were annotated with 26 core SDOH-related entity types using pre-trained named entity recognition (NER) models, human review, and data augmentation to improve quality, diversity and representation of entity types. An NLP pipeline integrating NER, natural language inference (NLI), trigram and frequency analyses was developed to extract and analyze these entities. Both encoder-only transformer models and RNN-based models were assessed for the NER objective. Fine-tuned encoder-only BERT models outperformed traditional RNN-based models in generalizability to distinct sentence structures and greater class sparsity. Exploratory analysis revealed variability in entity richness, with prevalent entities like condition, age, and access to care, and underrepresentation of sensitive categories like race and housing status. Trigram analysis highlighted frequent co-occurrences among entities, including age, gender, and condition. The NLI objective (entailment and contradiction analysis) showed attributes like "Experienced violence or abuse" and "Has medical insurance" had high entailment rates (82.4%-80.3%), while attributes such as "Is female-identifying," "Is married," and "Has a terminal condition" exhibited high contradiction rates (70.8%-98.5%).

Materials exhibiting a martensitic phase transition are essential for applications in shape memory alloys, actuators and sensors. Hexamethylbenzene (HMB) has long been considered as a classical example of ferroelastic organic crystals since Mnyukh's pioneering work in 1970s. However, the atomistic mechanism underlying this phase transition has never been clarified. In this work, we present a direct molecular dynamics simulation to investigate the phase transition mechanism in HMB. For the first time, we report a simulation results that can accurately reproduce both the transition temperature and hysteresis loop observed in previous experimental studies. By analyzing the MD trajectories, the potential energy surface, we identified that a low-barrier atomic sliding mode along the close-packed (11$\overline{1}$) plane of the low-temperature phase is the key to trigger the phase transition at the critical temperature window. This is further confirmed by the observed continuous softening of shear modulus around the transition window. Our results demonstrate that the integration of various atomistic modeling techniques can provide invaluable insight into the martensitic phase transition mechanisms in organic crystals and guide the development of new organic martensites., Comment: 9 pages, 9 figures

This study proposes a new full-field approach for modeling grain boundary pinning by second phase particles in two-dimensional polycrystals. These particles are of great importance during thermomechanical treatments, as they produce deviations from the microstructural evolution that the alloy produces in the absence of particles. This phenomenon, well-known as Smith-Zener pinning, is widely used by metallurgists to control the grain size during the metal forming process of many alloys. Predictive tools are then needed to accurately model this phenomenon. This article introduces a new methodology for the simulation of microstructural evolutions subjected to the presence of second phase particles. The methodology employs a Lagrangian 2D front-tracking methodology, while the particles are modeled using discretized circular shapes or pinning nodes. The evolution of the particles can be considered and modeled using a constant velocity of particle shrinking. This approach has the advantages of improving the limited description made of the phenomenon in vertex approaches, to be usable for a wide range of second-phase particle sizes and to improve calculation times compared to front-capturing type approaches.

Proper characterization of quantum correlations in a multimode optical state is critical for applications in quantum information science; however, the most common entanglement measurements can lead to an incomplete state reconstruction. This is the case for the ubiquitous spectral measurement of field quadratures for which a full characterization of the quantum correlations between optical beams is not possible. We demonstrate this effect in twin beams generated through parametric amplification by four-wave mixing in hot rubidium vapor, showing the role of a frequency dependent gain response. We implement a resonator-based measurement that reveals entanglement between beams that is otherwise hidden by usual spectral measurements. Additionally, this system shows how the phase shifts between the carrier and the sidebands on the involved fields affect the observation of entanglement for different entanglement witnesses, demonstrating the relevance of making a complete state tomography.

Quantum imaging with undetected photons (QIUP) is an emerging technique that decouples the processes of illuminating an object and projecting its image. The properties of the illuminating and detected light can thus be simultaneously optimised for both contrast on a sample and sensitivity on a camera. Here, we combine QIUP with computed tomography to enable three-dimensional (3D) infrared imaging. The image data is registered with a standard silicon camera at a wavelength of 810 nm, but the extracted 3D images map the sample's absorption at a wavelength of 1550 nm, well beyond the camera's sensitivity. Quantum Undetected Optical Projection Tomography (QUOPT) enables label-free volumetric sensing at difficult to detect wavelengths, such as those that allow molecular imaging contrast, or those within the infrared biological transmission windows.

We establish integrality and congruence properties for the Eisenstein-Kronecker cocycle of Bergeron, Charollois and Garc\'ia introduced in [arXiv:2107.01992v2 [math.NT]]. As a consequence, we recover the integrality of the critical values of Hecke $L$-functions over imaginary quadratic fields in the split case. Additionally, we construct a $p$-adic measure that interpolates these critical values.

The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (\ie, multilingual LLMs) is bottlenecked by the lack of high-quality evaluation resources in languages other than English. Moreover, current practices in multilingual benchmark construction often translate English resources, ignoring the regional and cultural knowledge of the environments in which multilingual systems would be used. In this work, we construct an evaluation suite of 197,243 QA pairs from local exam sources to measure the capabilities of multilingual LLMs in a variety of regional contexts. Our novel resource, INCLUDE, is a comprehensive knowledge- and reasoning-centric benchmark across 44 written languages that evaluates multilingual LLMs for performance in the actual language environments where they would be deployed.

We present a phenomenology study probing pair production of supersymmetric charginos and neutralinos ("electroweakinos") with the vector boson fusion (VBF) topology in proton-proton collisions at CERN's Large Hadron Collider (LHC). In particular, we examine the compressed-mass spectrum phase space that has been traditionally challenging due to experimental constraints. The final states considered have two jets, large missing transverse momentum, and one, two, or three light leptons. Different model scenarios are considered for the production and decays of the electroweakinos. A novel high-performance and interpretable sequential attention-based machine learning algorithm is employed for signal-background discrimination and is observed to significantly improve signal sensitivity over traditional methods. We report expected signal significances for integrated luminosities of $137$, $300$, and $3000$ $\textrm{fb}^{-1}$ corresponding to the current data acquired at the LHC, expectation for the end of Run 3, and the expectation for the high-luminosity LHC. Our methodology results in projected 95\% confidence level bounds that cover chargino masses up to 1.1 TeV in compressed-mass spectrum scenarios within the R-parity conserving minimal supersymmetric standard model. This parameter space, currently beyond the reach of ATLAS and CMS searches at the LHC, is traditionally challenging to explore due to significant Standard Model backgrounds and low signal cross-sections.

Non-commutative geometry has significantly contributed to quantum mechanics by providing mathematical tools to extract topological and geometrical information from these systems. This thesis explores the methods used by Jean Bellissard and collaborators for analyzing homogeneous materials. The focus is on two topological algebras that extend Fourier analysis over $\mathbb{T}^d$ to study tight-binding models for homogeneous materials. These algebras, generalizations of $C(\mathbb{T}^d)$ and $C^{\infty}(\mathbb{T}^d)$, are considered anon-commutative smooth manifold, referred to as the Non-Commutative Brillouin Torus. Techniques from Fourier analysis, such as Fourier coefficients and Fej\'er summation, are adapted to this context, capturing the topological and smooth structure of the non-commutative manifold. The topological structure is represented by a C* algebra, while the smooth structure is captured by a smooth sub-algebra, forming the basis for constructing topological invariants of Hamiltonians through continuous cyclic cohomology. Additionally, the K theory of C* algebras and smooth sub-algebras is a crucial tool for studying topological invariants of Hamiltonians. Smooth sub-algebras share a functional calculus with C* algebras, providing sufficient information to study their K theory. This relationship is vital for identifying topological invariants of Hamiltonians, particularly under conditions like low temperature and electron density, which contribute to the quantization of transversal conductivity in homogeneous materials. This thesis aims to be a valuable resource for newcomers to the field.

A model based on a $U(1)_{T^3_R}$ extension of the Standard Model can address the mass hierarchy between generations of fermions, explain thermal dark matter abundance, and the muon $g - 2$, $R_{(D)}$, and $R_{(D^*)}$ anomalies. The model contains a light scalar boson $\phi'$ and a heavy vector-like quark $\chi_\mathrm{u}$ that can be probed at CERN's Large Hadron Collider (LHC). We perform a phenomenology study on the production of $\phi'$ and ${\chi}_u$ particles from proton-proton $(\mathrm{pp})$ collisions at the LHC at $\sqrt{s}=13.6$ TeV, primarily through $g{-g}$ and $t{-\chi_\mathrm{u}}$ fusion. We work under an effective field theory approach, in which the $\chi_\mathrm{u}$ and $\phi'$ masses are free parameters. We perform a phenomenological analysis considering $\chi_\mathrm{u}$ final states to b-quarks, muons, and neutrinos, and $\phi'$ decays to $\mu^+\mu^-$. A machine learning algorithm is used to maximize the signal sensitivity, considering an integrated luminosity of $3000$ $\textrm{fb}^{-1}$. The proposed methodology can be a key mode for discovery over a large mass range, including low masses, traditionally considered difficult due to experimental constraints.

This booklet on Active Assisted Living (AAL) technologies has been created as part of the GoodBrother COST Action, which has run from 2020 to 2024. COST Actions are European research programs that promote collaboration across borders, uniting researchers, professionals, and institutions to address key societal challenges. GoodBrother focused on ethical and privacy concerns surrounding video and audio monitoring in care settings. The aim was to ensure that while AAL technologies help older adults and vulnerable individuals, their privacy and data protection rights remain a top priority. This booklet is designed to guide you through the role that AAL technologies play in improving the quality of life for older adults, caregivers, and people with disabilities. AAL technologies offer tools for those facing cognitive or physical challenges. They can enhance independence, assist with daily routines, and promote a safer living environment. However, the rise of these technologies also brings important questions about data protection and user autonomy. This resource is intended for a wide audience, including end users, caregivers, healthcare professionals, and policymakers. It provides practical guidance on integrating AAL technologies into care settings while safeguarding privacy and ensuring ethical use. The insights offered here aim to empower users and caregivers to make informed choices that enhance both the quality of care and respect for personal autonomy.

Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data are essential for making reliable inferences. In this paper, we propose the use of the Conway-Maxwell-Poisson (CMP) model for analyzing count data in sports. The CMP model offers flexibility in modeling data with different levels of dispersion. Here we consider a bivariate CMP model that models the potential correlation between home and away scores by incorporating a random effect specification. We illustrate the advantages of the CMP model through simulations. We then analyze data from baseball and soccer games before, during, and after the COVID-19 pandemic. The performance of our proposed CMP model matches or outperforms standard Poisson and Negative Binomial models, providing a good fit and an accurate estimation of the observed effects in count data with any level of dispersion. The results highlight the robustness and flexibility of the CMP model in analyzing count data in sports, making it a suitable default choice for modeling a diverse range of count data types in sports, where the data dispersion may vary., Comment: Journal of Quantitative Analysis in Sports (in press)

We propose a standalone monocular visual Simultaneous Localization and Mapping (vSLAM) initialization pipeline for autonomous space robots. Our method, a state-of-the-art factor graph optimization pipeline, extends Structure from Small Motion (SfSM) to robustly initialize a monocular agent in spacecraft inspection trajectories, addressing visual estimation challenges such as weak-perspective projection and center-pointing motion, which exacerbates the bas-relief ambiguity, dominant planar geometry, which causes motion estimation degeneracies in classical Structure from Motion, and dynamic illumination conditions, which reduce the survivability of visual information. We validate our approach on realistic, simulated satellite inspection image sequences with a tumbling spacecraft and demonstrate the method's effectiveness over existing monocular initialization procedures., Comment: 6 pages, 1 page for references, 6 figures, 1 table, IEEEtran format. This work has been submitted to ACC for possible publication as an invited session paper

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

The searches for an underlying pattern in neutrino masses have motivated different proposals for textures in the neutrino mass matrix, which is also related to particular arranges of the mixing matrix. The current precise determinations of neutrino mixings have discarded some of the most studied proposals, such as the one involving a $\mu-\tau$ exchange symmetry. In this work, we investigate the relation of a still allowed $\mu-\tau$ reflection symmetry with the constraints of a magic pattern in the neutrino mass matrix. We show that both conditions cannot be fulfilled simultaneously in an exact but rather in an approximate way. Such considerations are used to restrict the values of CP-violating phases and some observables, like the neutrinoless double beta decay amplitude and the solar angle, that can be explored with forthcoming results., Comment: 10 pages, 3 figures, 1 table

A projective rectangle is like a projective plane that may have different lengths in two directions. We develop properties of the graph of lines, in which adjacency means having a common point, especially its strong regularity and clique structure. The main construction of projective rectangles, stated in a previous paper, gives rectangles whose graph of lines is a known strongly regular bilinear forms graph. That fact leads to a proof that the main construction does produce projective rectangles, and also gives a new representation of bilinear forms graphs. We conclude by mentioning a few simple graph properties, such as the chromatic number, which is not known, and a partial geometry obtained from the graph., Comment: 17 pp., 2 figures

Inspired by the ancient spiral constructed by the greek philosopher Theodorus which is based on concatenated right triangles, we have created a spiral. In this spiral, called \emph{Fibonacci--Theodorus}, the sides of the triangles have lengths corresponding to Fibonacci numbers. Towards the end of the paper, we present a generalized method applicable to second-order recurrence relations. Our exploration of the Fibonacci--Theodorus spiral aims to address a variety of questions, showcasing its unique properties and behaviors. For example, we study topics such as area, perimeter, and angles. Notably, we establish a relationship between the ratio of two consecutive areas and the golden ratio, a pattern that extends to angles sharing a common vertex. Furthermore, we present some asymptotic results. For instance, we demonstrate that the sum of the first $n$ areas comprising the spiral approaches a multiple of the sum of the initial $n$ Fibonacci numbers. Moreover, we provide a sequence of open problems related to all spiral worked in this paper. Finally, in his work Hahn, Hahn observed a potential connection between the golden ratio and the ratio of areas between spines of lengths $\sqrt{F_{n+1}}$ and $\sqrt{F_{n+2}-1}$ and the areas between spines of lengths $\sqrt{F_{n}}$ and $\sqrt{F_{n+1}-1}$ in the Theodorus spiral. However, no formal proof has been provided in his work. In this paper, we provide a proof for Hahn's conjecture., Comment: Accepted for publication by Proceedings of Fibonacci Quarterly

A projective rectangle is like a projective plane that has different lengths in two directions. We develop harmonic conjugation in projective rectangles. We construct projective rectangles in some harmonic matroids (matroids where harmonic conjugation is defined on every collinear point triple), such as Desarguesian projective planes of finite characteristic, by harmonic conjugation from extended lift matroids based on finite fields. Similar results follow for countable fields with characteristic $0$. We also show that projective rectangles are almost harmonic matroids., Comment: 29 pp., 8 figures. V2: minor changes

Accurate determination of the geothermal gradient is critical for assessing the geothermal energy potential of a given region. Of particular interest is the case of Colombia, a country with abundant geothermal resources. A history of active oil and gas exploration and production has left drilled boreholes in different geological settings, providing direct measurements of the geothermal gradient. Unfortunately, large regions of the country where geothermal resources might exist lack such measurements. Indirect geophysical measurements are costly and difficult to perform at regional scales. Computational thermal models could be constructed, but they require very detailed knowledge of the underlying geology and uniform sampling of subsurface temperatures to be well-constrained. We present an alternative approach that leverages recent advances in supervised machine learning and available direct measurements to predict the geothermal gradient in regions where only global-scale geophysical datasets and course geological knowledge are available. We find that a Gradient Boosted Regression Tree algorithm yields optimal predictions and extensively validate the trained model. We show that predictions of our model are within 12% accuracy and that independent measurements performed by other authors agree well with our model. Finnally, we present a geothermal gradient map for Colombia that highlights regions where futher exploration and data collection should be performed., Comment: This is the version we re-submitted to the journal after addressing all the peer review requirements

An ultra-compact wearable antenna, for electronic travel aid (ETA) applications, is presented. An AMC-backed twin arrow antenna, operative in the 24.05-24.25 GHz frequency band, has been designed for imaging systems supporting ETA. Artificial Magnetic Conductor (AMC) is combined with the antenna with the aim of reducing the backward radiation to the wearing person while improving its radiation properties and bandwidth, all this without increasing the initial area of the antenna. Prototypes of the AMC-antenna have been fabricated and measured. In order to test its performance for the application, imaging have been conducted by means of synthetic aperture radar (SAR) techniques by placing the antenna in the arm of a user to take advantage of natural body movement. Electromagnetic images have been obtained and the target has been identified, demonstrating the suitability of the AMC-antenna for the ETA system.

This work presents a simple power converter, without any high voltage transformer, able to supply and control a plasma jet based on dielectric barrier discharge. The converter, operating in pulsed current mode, requires a single power switch and is fed by a low voltage DC source. It can deliver very short duration pulses to the plasma jet with high current amplitude. The operating principle is explained by means of the state plane analysis and is validated with simulations and experimental results. The equations provided allow for the calculation during the design stage of important characteristics of the plasma jet as the peak voltage and the duration of the pulses. The power can be easily adjusted during experimentation to comply with the desired appearance of the plasma jet.

Gaussian processes are probabilistic models that are commonly used as functional priors in machine learning. Due to their probabilistic nature, they can be used to capture the prior information on the statistics of noise, smoothness of the functions, and training data uncertainty. However, their computational complexity quickly becomes intractable as the size of the data set grows. We propose a Hilbert space approximation-based quantum algorithm for Gaussian process regression to overcome this limitation. Our method consists of a combination of classical basis function expansion with quantum computing techniques of quantum principal component analysis, conditional rotations, and Hadamard and Swap tests. The quantum principal component analysis is used to estimate the eigenvalues while the conditional rotations and the Hadamard and Swap tests are employed to evaluate the posterior mean and variance of the Gaussian process. Our method provides polynomial computational complexity reduction over the classical method., Comment: 9 pages, 5 figures

In this study, the classical two-dimensional potential $V_N=\frac{1}{2}\,m\,\omega^2\,r^2 + \frac{1}{N}\,r^N\,\sin(N\,\theta)$, $N \in {\mathbb Z}^+$, is considered. At $N=1,2$, the system is superintegrable and integrable, respectively, whereas for $N>2$ it exhibits a richer chaotic dynamics. For instance, at $N=3$ it coincides with the H\'enon-Heiles system. The periodic, quasi-periodic and chaotic motions are systematically characterized employing time series, Poincar\'e sections, symmetry lines and the largest Lyapunov exponent as a function of the energy $E$ and the parameter $N$. Concrete results for the lowest cases $N=3,4$ are presented in complete detail. This model is used as a benchmark system to estimate the accuracy of the Sparse Identification of Nonlinear Dynamical Systems (SINDy) method, a data-driven algorithm which reconstructs the underlying governing dynamical equations. We pay special attention at the transition from regular motion to chaos and how this influences the precision of the algorithm. In particular, it is shown that SINDy is a robust and stable tool possessing the ability to generate non-trivial approximate analytical expressions for periodic trajectories as well.