Search

This paper investigates uniqueness results for perturbed periodic Schr\"odinger operators on $\mathbb{Z}^d$. Specifically, we consider operators of the form $H = -\Delta + V + v$, where $\Delta$ is the discrete Laplacian, $V: \mathbb{Z}^d \rightarrow \mathbb{R}$ is a periodic potential, and $v: \mathbb{Z}^d \rightarrow \mathbb{C}$ represents a decaying impurity. We establish quantitative conditions under which the equation $-\Delta u + V u + v u = \lambda u$, for $\lambda \in \mathbb{C}$, admits only the trivial solution $u \equiv 0$. Key applications include the absence of embedded eigenvalues for operators with impurities decaying faster than any exponential function and the determination of sharp decay rates for eigenfunctions. Our findings extend previous works by providing precise decay conditions for impurities and analyzing different spectral regimes of $\lambda$., Comment: 21 pages

We present the first implementation of text-level quantum natural language processing, a field where quantum computing and AI have found a fruitful intersection. We focus on the QDisCoCirc model, which is underpinned by a compositional approach to rendering AI interpretable: the behaviour of the whole can be understood in terms of the behaviour of parts, and the way they are put together. Interpretability is crucial for understanding the unwanted behaviours of AI. By leveraging the compositional structure in the model's architecture, we introduce a novel setup which enables 'compositional generalisation': we classically train components which are then composed to generate larger test instances, the evaluation of which asymptotically requires a quantum computer. Another key advantage of our approach is that it bypasses the trainability challenges arising in quantum machine learning. The main task that we consider is the model-native task of question-answering, and we handcraft toy scale data that serves as a proving ground. We demonstrate an experiment on Quantinuum's H1-1 trapped-ion quantum processor, which constitutes the first proof of concept implementation of scalable compositional QNLP. We also provide resource estimates for classically simulating the model. The compositional structure allows us to inspect and interpret the word embeddings the model learns for each word, as well as the way in which they interact. This improves our understanding of how it tackles the question-answering task. As an initial comparison with classical baselines, we considered transformer and LSTM models, as well as GPT-4, none of which succeeded at compositional generalisation., Comment: 60 pages, 50 figures

Recent observations on the large-scale structure of the universe indicate that the cosmological constant cannot be the definitive answer to the nature of dark energy. Therefore, it is a good time to propose alternatives to understand the late-accelerated expansion of the universe. In this work we study the possibility that the acceleration of space-time is due to the topology of the universe. We assume that the topology of the universe is a principal fiber bundle whose base space is our 4-dimensional spacetime and the fiber is an $N$-dimensional Lie group that evolves with time. For the base space we consider a homogeneous and isotropic spacetime, we find that the base space is currently accelerating for $1 < N$ compact semi-simple Lie groups whose scale-factors are equal and for $1 < N$ non-Abelian Lie groups whose scale-factors are different and as long as its structure constants satisfy some conditions. However when we study the evolution of the density parameters these differ from the evolution within the $\Lambda$CDM model, this led us to think in the possibility of use a different group as fiber in order to obtain the right evolution of the density parameters. We conclude that it is possible that the accelerated expansion of the universe is due to consider a different topology of the universe as a principal fiber bundle., Comment: 16 pages, 5 figures

Recent developments in online communication and their usage in everyday life have caused an explosion in the amount of a new genre of text data, short text. Thus, the need to classify this type of text based on its content has a significant implication in many areas. Online debates are no exception, once these provide access to information about opinions, positions and preferences of its users. This paper aims to use data obtained from online social conversations in Portuguese schools (short text) to observe behavioural trends and to see if students remain engaged in the discussion when stimulated. This project used the state of the art (SoA) Machine Learning (ML) algorithms and methods, through BERT based models to classify if utterances are in or out of the debate subject. Using SBERT embeddings as a feature, with supervised learning, the proposed model achieved results above 0.95 average accuracy for classifying online messages. Such improvements can help social scientists better understand human communication, behaviour, discussion and persuasion.

The distinguishability between two quantum states can be defined in terms of their trace distance. The operational meaning of this definition involves a maximization over measurement projectors. Here we introduce an alternative definition of distinguishability which, instead of projectors, is based on maximization over normalized states (density matrices). It is shown that this procedure leads to a distance (between two states) that, in contrast to the usual approach based on a 1-norm, is based on an infinite-norm. Properties such as convexity, monotonicity, and invariance under unitary transformations are fulfilled. Equivalent operational implementations based on maximization over classical probabilities and hypothesis testing scenarios are also established. When considering the action of completely positive transformations contractivity is only granted for unital maps. This feature allows us to introduce a measure of the quantumness of non-unital maps that can be written in terms of the proposed distinguishability measure and corresponds to the maximal possible deviation from contractivity. Particular examples sustain the main results and conclusions., Comment: 13 pages, 4 figures

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics., Comment: 12 pages, 6 figures

Biotite, an iron-rich mineral belonging to the trioctahedral mica group, is a naturally abundant layered material (LM) exhibiting attractive electronic properties for application in nanodevices. Biotite stands out as a non-degradable LM under ambient conditions, featuring high-quality basal cleavage, a significant advantage for van der Waals heterostructure (vdWH) applications. In this work, we present the micro-mechanical exfoliation of biotite down to monolayers (1Ls), yielding ultrathin flakes with large areas and atomically flat surfaces. To identify and characterize the mineral, we conducted a multi-elemental analysis of biotite using energy-dispersive spectroscopy mapping. Additionally, synchrotron infrared nano-spectroscopy was employed to probe its vibrational signature in few-layer form, with sensitivity to the layer number. We have also observed good morphological and structural stability in time (up to 12 months) and no important changes in their physical properties after thermal annealing processes in ultrathin biotite flakes. Conductive atomic force microscopy evaluated its electrical capacity, revealing an electrical breakdown strength of approximately 1 V/nm. Finally, we explore the use of biotite as a substrate and encapsulating LM in vdWH applications. We have performed optical and magneto-optical measurements at low temperatures. We find that ultrathin biotite flakes work as a good substrate for 1L-MoSe2, comparable to hexagonal boron nitride flakes, but it induces a small change of the 1L-MoSe2 g-factor values, most likely due to natural impurities on its crystal structure. Furthermore, our results show that biotite flakes are useful systems to protect sensitive LMs such as black phosphorus from degradation for up to 60 days in ambient air. Our study introduces biotite as a promising, cost-effective LM for the advancement of future ultrathin nanotechnologies., Comment: 20 pages, 4 figures

An infinite game on the set of real numbers appeared in Matthew Baker's work [Math. Mag. 80 (2007), no. 5, pp. 377--380] in which he asks whether it can help characterize countable subsets of the reals. This question is in a similar spirit to how the Banach-Mazur Game characterizes meager sets in an arbitrary topological space. In a recent paper, Will Brian and Steven Clontz prove that in Baker's game, Player II has a winning strategy if and only if the payoff set is countable. They also asked if it is possible, in general linear orders, for Player II to have a winning strategy on some uncountable set. To this we give a positive answer and moreover construct, for every infinite cardinal $\kappa$, a dense linear order of size $\kappa$ on which Player II has a winning strategy on all payoff sets. We finish with some future research questions, further underlining the difficulty in generalizing the characterization of Brian and Clontz to linear orders.

Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors; for instance, pulse oximeters overestimate the arterial oxygen saturation of darker-skinned individuals, leading to worse outcomes. The impact of this bias in machine learning (ML) models remains unclear. This study addresses the technical challenges of quantifying the impact of medical device bias in downstream ML. Our experiments compare a "perfect world", without pulse oximetry bias, using SaO2 (blood-gas), to the "actual world", with biased measurements, using SpO2 (pulse oximetry). Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO2 are a "control" and models using SpO2 a "treatment". The blood-gas oximetry linked dataset was a suitable test-bed, containing 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned with a wide array of clinical features and outcomes. We studied three classification tasks: in-hospital mortality, respiratory SOFA score in the next 24 hours, and SOFA score increase by two points. Models using SaO2 instead of SpO2 generally showed better performance. Patients with overestimation of O2 by pulse oximetry of > 3% had significant decreases in mortality prediction recall, from 0.63 to 0.59, P < 0.001. This mirrors clinical processes where biased pulse oximetry readings provide clinicians with false reassurance of patients' oxygen levels. A similar degradation happened in ML models, with pulse oximetry biases leading to more false negatives in predicting adverse outcomes., Comment: 10 pages; accepted at MICCAI's Third Workshop on Applications of Medical AI (2024)

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (conv5-FC3, ResNet and SECNN) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM/QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the conv5-FC3 network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:016

Observations with the Gaia satellite have confirmed that the satellite galaxies of the Milky Way are not distributed as homogeneously as expected. The same occurs in galaxies such as Andromeda and Centaurus A, where satellite galaxies around their host galaxies have been observed to have orbits aligned perpendicular to the galactic plane of the host galaxy. This problem is known for the Milky Way as Vast Polar Structure (VPOS). The Scalar Field Dark Matter Field (SFDM), also known as Ultralight-, Fuzzy-, BEC-, Axion-dark matter, proposes dark matter is a scalar field, which in the non-relativistic limit follows the Schr\"odinger equation coupled to the Poisson equation. Although the SF here is classical, the Schr\"odinger equation contains a ground and excited states as part of its nature. In this work, we show that such quantum character of the SFDM can naturally explain the VPOS observed in galaxies. By taking into account the finite temperature corrections for a complex, self-interacting SF at very early epochs of the Universe, we show that with the ground and first excited states in the Newtonian limit, we can fit the rotation curves of the galaxies. With the best-fit parameters obtained, we can explain the VPOS. We do this with particular galaxies, such as the Milky Way, Andromeda, Centaurus A, and six other galaxies whose satellites have been observed. This result shows that the multistate SFDM is not distributed homogeneously around the galaxy, and therefore might explain the anisotropic distribution of the satellite galaxies. According to this result, this could be a general characteristic of the galaxies in the Universe. Finally, we also show how the scale of each galaxy depends on a parameter that is determined by the final temperature of the SF of the galaxy halo under study. This explains why different galaxies with SFDM give different values of the mass of the SF., Comment: Submitted to review

This study presents a comprehensive analysis of the band structure in NiPS$_3$, a van der Waals layered antiferromagnet, utilizing high-resolution synchrotron-based angle-resolved photoemission spectroscopy (ARPES) and corroborative density functional theory (DFT) calculations. By tuning the parameters of the light source, we obtained a very clear and wide energy range band structure of NiPS$_3$. Comparison with DFT calculations allows for the identification of the orbital character of the observed bands. Our DFT calculations perfectly match the experimental results, and no adaptations were made to the calculations based on the experimental outcomes. The appearance of novel electronic structure upon alkali metal dosing (AMD) were also obtained in this ARPES study. Above valence band maximum, structure of conduction bands and bands from defect states were firstly observed in NiPS$_3$. We provide the direct determination of the band gap of NiPS$_3$ as 1.3 eV from the band structure by AMD. In addition, detailed temperature dependent ARPES spectra were obtained across a range that spans both below and above the N\'eel transition temperature of NiPS$_3$. We found that the paramagnetic and antiferromagnetic states have almost identical spectra, indicating the highly localized nature of Ni $d$ states., Comment: 4 figures

Electronic health records (EHRs) contain vast amounts of complex data, but harmonizing and processing this information remains a challenging and costly task requiring significant clinical expertise. While large language models (LLMs) have shown promise in various healthcare applications, their potential for abstracting medical concepts from EHRs remains largely unexplored. We introduce EHRmonize, a framework leveraging LLMs to abstract medical concepts from EHR data. Our study uses medication data from two real-world EHR databases to evaluate five LLMs on two free-text extraction and six binary classification tasks across various prompting strategies. GPT-4o's with 10-shot prompting achieved the highest performance in all tasks, accompanied by Claude-3.5-Sonnet in a subset of tasks. GPT-4o achieved an accuracy of 97% in identifying generic route names, 82% for generic drug names, and 100% in performing binary classification of antibiotics. While EHRmonize significantly enhances efficiency, reducing annotation time by an estimated 60%, we emphasize that clinician oversight remains essential. Our framework, available as a Python package, offers a promising tool to assist clinicians in EHR data abstraction, potentially accelerating healthcare research and improving data harmonization processes., Comment: submitted for review, total of 10 pages

Gravitational waves (GWs) are signals that propagate across large distances in the Universe, and thus, they bring information on the cosmic history. GW sources are at the same time distance indicators and tracers of the matter field. Events generated by binary systems can be divided into bright standard sirens, when followed by electromagnetic transients from which the redshift of the source can be measured, and the more numerous dark standard sirens, when counterparts are not available. In this proceeding, I will discuss some methods for testing the cosmological model using either bright or dark sirens and their combinations with other cosmological probes, focusing on some of my own recent contributions., Comment: Contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation.

The superconducting diode effect (SDE) is a magneto-electric phenomenon where an external magnetic field imparts a non-zero center-of-mass momentum to Cooper pairs, either facilitating or hindering the flow of supercurrent depending on its direction. We propose that quantum spin Hall insulator (QSHI)-based Josephson junctions can serve as versatile platforms for non-dissipative electronics exhibiting the SDE when triggered by a phase bias and an out-of-plane magnetic field. By computing the contributions from Andreev bound states and the continuum of quasi-particle states, we provide both numerical and analytical results scrutinizing various aspects of the SDE, including its quality Q-factor. The maximum value of the $Q$-factor is found to be universal at low (zero) temperatures, which ties its origin to underlying topological properties that are independent of the junction's specific details. As the magnetic field increases, the SDE diminishes due to the closing of the induced superconducting gap caused by orbital effects. To observe the SDE, the QSHI-based Josephson junction must be designed so that its edges are transport-wise non-equivalent. Additionally, we explore the SDE in a more exotic yet realistic scenario, where the fermionic ground-state parity of the Josephson junction remains conserved while driving a current. In this 4$\pi$-periodic situation, we predict an enhancement of the SDE compared to its 2$\pi$-periodic, parity-unconstrained counterpart., Comment: 9 pages, 9 figures

A planar Josephson junction is a versatile platform to realize topological superconductivity over a large parameter space and host Majorana bound states. With a change in Zeeman field, this system undergoes a transition from trivial to topological superconductivity accompanied by a jump in the superconducting phase difference between the two superconductors. A standard model of these Josephson junctions, which can be fabricated to have a nearly perfect interfacial transparency, predicts a simple universal behavior. In that model, at the same value of Zeeman field for the topological transition, there is a $\pi$ phase jump and a minimum in the critical superconducting current, while applying a controllable phase difference yields a diamond-shaped topological region as a function of that phase difference and a Zeeman field. In contrast, even for a perfect interfacial transparency, we find a much richer and nonuniversal behavior as the width of the superconductor is varied or the Dresselhaus spin-orbit coupling is considered. The Zeeman field for the phase jump, not necessarily $\pi$, is different from the value for the minimum critical current, while there is a strong deviation from the diamond-like topological region. These Josephson junctions show a striking example of a nonreciprocal transport and superconducting diode effect, revealing the importance of our findings not only for topological superconductivity and fault-tolerant quantum computing, but also for superconducting spintronics., Comment: Invited to APL Special Topic Issue: "Josephson Junctions and Related Proximity Effects: From Basic Science to Emerging Applications in Advanced Technologies", accepted version

Progress in the realisation of reliable large-scale quantum computers has motivated research into the design of quantum machine learning models. We present Quixer: a novel quantum transformer model which utilises the Linear Combination of Unitaries and Quantum Singular Value Transform primitives as building blocks. Quixer operates by preparing a superposition of tokens and applying a trainable non-linear transformation to this mix. We present the first results for a quantum transformer model applied to a practical language modelling task, obtaining results competitive with an equivalent classical baseline. In addition, we include resource estimates for evaluating the model on quantum hardware, and provide an open-source implementation for classical simulation. We conclude by highlighting the generality of Quixer, showing that its parameterised components can be substituted with fixed structures to yield new classes of quantum transformers., Comment: 17 pages, 8 figures

Creating engaging narratives from visual data is crucial for automated digital media consumption, assistive technologies, and interactive entertainment. This survey covers methodologies used in the generation of these narratives, focusing on their principles, strengths, and limitations. The survey also covers tasks related to automatic story generation, such as image and video captioning, and visual question answering, as well as story generation without visual inputs. These tasks share common challenges with visual story generation and have served as inspiration for the techniques used in the field. We analyze the main datasets and evaluation metrics, providing a critical perspective on their limitations.

We investigate strategies for reaching the ultimate limit on the precision of frequency estimation when the number of probes used in each run of the experiment is fixed. That limit is set by the quantum Cram\'er-Rao bound (QCRB), which predicts that the use of maximally entangled probes enhances the estimation precision, when compared with the use of independent probes. However, the bound is only achievable if the statistical model used in the estimation remains identifiable throughout the procedure. This in turn sets different limits on the maximal sensing time used in each run of the estimation procedure, when entangled and independent probes are used. When those constraints are taken into account, one can show that, when the total number of probes and the total duration of the estimation process are counted as fixed resources, the use of entangled probes is, in fact, disadvantageous when compared with the use of independent probes. In order to counteract the limitations imposed on the sensing time by the requirement of identifiability of the statistical model, we propose a time-adaptive strategy, in which the sensing time is adequately increased at each step of the estimation process, calculate an attainable error bound for the strategy and discuss how to optimally choose its parameters in order to minimize that bound. We show that the proposed strategy leads to much better scaling of the estimation uncertainty with the total number of probes and the total sensing time than the traditional fixed-sensing-time strategy. We also show that, when the total number of probes and the total sensing time are counted as resources, independent probes and maximally entangled ones have now the same performance, in contrast to the non-adaptive strategy, where the use of independent is more advantageous than the use of maximally entangled ones., Comment: 15 pages

Objectives: This study aims to systematically review the literature on the computational processing of the language of pain, or pain narratives, whether generated by patients or physicians, identifying current trends and challenges. Methods: Following the PRISMA guidelines, a comprehensive literature search was conducted to select relevant studies on the computational processing of the language of pain and answer pre-defined research questions. Data extraction and synthesis were performed to categorize selected studies according to their primary purpose and outcome, patient and pain population, textual data, computational methodology, and outcome targets. Results: Physician-generated language of pain, specifically from clinical notes, was the most used data. Tasks included patient diagnosis and triaging, identification of pain mentions, treatment response prediction, biomedical entity extraction, correlation of linguistic features with clinical states, and lexico-semantic analysis of pain narratives. Only one study included previous linguistic knowledge on pain utterances in their experimental setup. Most studies targeted their outcomes for physicians, either directly as clinical tools or as indirect knowledge. The least targeted stage of clinical pain care was self-management, in which patients are most involved. Affective and sociocultural dimensions were the least studied domains. Only one study measured how physician performance on clinical tasks improved with the inclusion of the proposed algorithm. Discussion: This review found that future research should focus on analyzing patient-generated language of pain, developing patient-centered resources for self-management and patient-empowerment, exploring affective and sociocultural aspects of pain, and measuring improvements in physician performance when aided by the proposed tools., Comment: 36 pages, 16 tables, 2 figures, systematic review

Thermophoresis is the migration of a particle due to a thermal gradient. Here, we theoretically uncover the quantum version of thermophoresis. As a proof of principle, we analytically find a thermophoretic force on a trapped quantum particle having three energy levels in $\Lambda$ configuration. We then consider a model of N sites, each coupled to its first neighbors and subjected to a local bath at a certain temperature, so as to show numerically how quantum thermophoresis behaves with increasing delocalization of the quantum particle. We discuss how negative thermophoresis and the Dufour effect appear in the quantum regime., Comment: 5 pages, 2 figures

Raster Forge is a Python library and graphical user interface for raster data manipulation and analysis. The tool is focused on remote sensing applications, particularly in wildfire management. It allows users to import, visualize, and process raster layers for tasks such as image compositing or topographical analysis. For wildfire management, it generates fuel maps using predefined models. Its impact extends from disaster management to hydrological modeling, agriculture, and environmental monitoring. Raster Forge can be a valuable asset for geoscientists and researchers who rely on raster data analysis, enhancing geospatial data processing and visualization across various disciplines.

Transition metal dichalcogenide (TMD) monolayers present a singular coupling in their spin and valley degrees of freedom. Moreover, by applying an external magnetic field it is possible to break the energy degeneracy between their K and $-$K valleys. Thus, this analogous valley Zeeman effect opens the possibility of controlling and distinguishing the spin and valley of charge carriers in TMDs by their optical transition energies, making these materials promising for the next generation of spintronic and photonic devices. However, the free excitons of pristine TMD monolayer samples present a moderate valley Zeeman splitting, which is measured by their g-factor values that are approximately $-4$. Therefore, for application purposes it is mandatory alternative excitonic states with higher magnetic responses. Here we investigate the valley Zeeman effect in aged WS$_{2}$ and WSe$_{2}$ grown monolayers by magneto-photoluminescence measurements at cryogenic temperatures. These samples present a lower energy defect-bound exciton emission related to defects adsorbed during the aging process. While the free excitons of these samples exhibit g-factors between $-3$ and $-4$, their defect-bound excitons present giant effective g-factor values of $-(25.0 \pm 0.2)$ and $-(19.1 \pm 0.2)$ for WS$_{2}$ and WSe$_{2}$ aged monolayers, respectively. In addition, we observe a significant spin polarization of charge carriers in the defective mid gap states induced by the external magnetic fields. We explain this spin polarized population in terms of a spin-flip transition mechanism, which is also responsible for the magnetic dependent light emission of the defect-bound exciton states. Our work sheds light in the potential of aged TMDs as candidates for spintronic based devices.

The integration of data science into Geographic Information Systems (GIS) has facilitated the evolution of these tools into complete spatial analysis platforms. The adoption of machine learning and big data techniques has equipped these platforms with the capacity to handle larger amounts of increasingly complex data, transcending the limitations of more traditional approaches. This work traces the historical and technical evolution of data science and GIS as fields of study, highlighting the critical points of convergence between domains, and underlining the many sectors that rely on this integration. A GIS application is presented as a case study in the disaster management sector where we utilize aerial data from Tr\'oia, Portugal, to emphasize the process of insight extraction from raw data. We conclude by outlining prospects for future research in integration of these fields in general, and the developed application in particular., Comment: The peer-reviewed version of this paper is published in IEEE Xplore at https://doi.org/10.1109/YEF-ECE62614.2024.10624902. This version is typeset by the author and differs only in pagination and typographical detail

Text clustering is an important method for organising the increasing volume of digital content, aiding in the structuring and discovery of hidden patterns in uncategorised data. The effectiveness of text clustering largely depends on the selection of textual embeddings and clustering algorithms. This study argues that recent advancements in large language models (LLMs) have the potential to enhance this task. The research investigates how different textual embeddings, particularly those utilised in LLMs, and various clustering algorithms influence the clustering of text datasets. A series of experiments were conducted to evaluate the impact of embeddings on clustering results, the role of dimensionality reduction through summarisation, and the adjustment of model size. The findings indicate that LLM embeddings are superior at capturing subtleties in structured language. OpenAI's GPT-3.5 Turbo model yields better results in three out of five clustering metrics across most tested datasets. Most LLM embeddings show improvements in cluster purity and provide a more informative silhouette score, reflecting a refined structural understanding of text data compared to traditional methods. Among the more lightweight models, BERT demonstrates leading performance. Additionally, it was observed that increasing model dimensionality and employing summarisation techniques do not consistently enhance clustering efficiency, suggesting that these strategies require careful consideration for practical application. These results highlight a complex balance between the need for refined text representation and computational feasibility in text clustering applications. This study extends traditional text clustering frameworks by integrating embeddings from LLMs, offering improved methodologies and suggesting new avenues for future research in various types of textual analysis.

Nowadays an ever-growing concerning phenomenon, the emergence of algorithmic biases that can lead to unfair models, emerges. Several debiasing approaches have been proposed in the realm of deep learning, employing more or less sophisticated approaches to discourage these models from massively employing these biases. However, a question emerges: is this extra complexity really necessary? Is a vanilla-trained model already embodying some ``unbiased sub-networks'' that can be used in isolation and propose a solution without relying on the algorithmic biases? In this work, we show that such a sub-network typically exists, and can be extracted from a vanilla-trained model without requiring additional training. We further validate that such specific architecture is incapable of learning a specific bias, suggesting that there are possible architectural countermeasures to the problem of biases in deep neural networks.

In this paper a thorough theoretical study of a new class of collective excitations, dubbed hyperbolic surface phonon plasmon polaritons, is performed. This new type of light-matter excitations are shown to have unique properties that allows to explore them both as the basis of ultra-sensitive devices to the dielectric nature of its surroundings. The system is a van der Waals heterostructure -- a layered metamaterial, composed of different 2D materials in direct contact one with another, namely graphene ribbons and hexagonal boron nitride slabs of nanometric size. In the paper we discuss the spectrum of these new class of excitations, the associated electromagnetic fields, the sensitivity to the dielectric function of its surroundings, and the absorption spectrum. All this is accomplished using an analytical model that considerably diminishes the computational burden, as well as elucidates the underling physical mechanism of the excitations supported by the device., Comment: 19 pages, 9 figures

The demand for unprecedented performance in the upcoming 6G wireless networks is fomenting the research on THz communications empowered by Reconfigurable Inteligent Surfaces (RISs). A wide range of use cases have been proposed, most of them, assuming high-level RIS models that overlook some of the hardware impairments that this technology faces. The expectation is that the emergent reconfigurable THz technologies will eventually overcome its current limitations. This disassociation from the hardware may mask nonphysical assumptions, perceived as hardware limitations. In this paper, a top-down approach bounded by physical constraints is presented, distilling from system-level specifications, hardware requirements, and upper bounds for the RIS-aided system performance. We consider D-band indoor and outdoor scenarios where a more realistic assessment of the state-of-the-art solution can be made. The goal is to highlight the intricacies of the design procedure based on sound assumptions for the RIS performance. For a given signal range and angular coverage, we quantify the required RIS size, number of switching elements, and maximum achievable bandwidth and capacity., Comment: 6 pages, 6 figures, submitted for a conference presentation

According to a particular interpretation of quantum mechanics, the causal role of human consciousness in the measuring process is called upon to solve a foundational problem called the "measurement problem". Traditionally, this interpretation is tied up with the metaphysics of substance dualism. As such, this interpretation of quantum mechanics inherits the dualist's mind-body problem. Our working hypothesis is that a process-based approach to the consciousness causes collapse interpretation (CCCI) -- leaning on Whitehead's solution to the mind-body problem -- offers a better metaphysical understanding of consciousness and its role in interpreting quantum mechanics. This article is the kickoff for such a research program in the metaphysics of science., Comment: Forthcoming in Manuscrito vol. 47, n.1, 2024. Special issue: "Scientific Process Ontology and Metaphysics". Url: https://www.scielo.br/j/man/i/2024.v47n1

Layered franckeite is a natural superlattice composed of two alternating layers of different compositions, SnS$_2$- and PbS-like. This creates incommensurability between the two species along the planes of the layers, resulting in spontaneous symmetry-break periodic ripples in the \textit{a}-axis orientation. Nevertheless, natural franckeite heterostructure has shown potential for optoelectronic applications mostly because it is a semiconductor with 0.7 eV bandgap, air-stable, and can be easily exfoliated down to ultrathin thicknesses. Here, we demonstrate that few-layer franckeite shows a highly anisotropic nonlinear optical response due to its lattice structure, which allow for the identification of the ripple axis. Moreover, we find that the highly anisotropic third-harmonic emission strongly varies with material thickness. These features are further corroborated by a theoretical nonlinear susceptibility model and the nonlinear transfer matrix method. Overall, our findings help to understand this material and propose a characterization method that could be used in other layered materials and heterostructures to assign their characteristic axes., Comment: 27 pages, 5 figures. The following article has been accepted by the Journal of Applied Physics. After it is published, it will be found by DOI: 10.1063/5.0186615

Wireless communications in the THz frequency band is an envisioned revolutionary technology for sixth Generation (6G) networks. However, such frequencies impose certain coverage and device design challenges that need to be efficiently overcome. To this end, the development of cost- and energy-efficient approaches for scaling these networks to realistic scenarios constitute a necessity. Among the recent research trends contributing to these objectives belongs the technology of Reconfigurable Intelligent Surfaces (RISs). In fact, several high-level descriptions of THz systems based on RISs have been populating the literature. Nevertheless, hardware implementations of those systems are still very scarce, and not at the scale intended for most envisioned THz scenarios. In this paper, we overview some of the most significant hardware design and signal processing challenges with THz RISs, and present a preliminary analysis of their impact on the overall link budget and system performance, conducted in the framework of the ongoing TERRAMETA project., Comment: 5 pages, 7 figures, EuCAP 2024

This work addresses weight optimization problem for fully-connected feed-forward neural networks. Unlike existing approaches that are based on back-propagation (BP) and chain rule gradient-based optimization (which implies iterative execution, potentially burdensome and time-consuming in some cases), the proposed approach offers the solution for weight optimization in closed-form by means of least squares (LS) methodology. In the case where the input-to-output mapping is injective, the new approach optimizes the weights in a back-propagating fashion in a single iteration by jointly optimizing a set of weights in each layer for each neuron. In the case where the input-to-output mapping is not injective (e.g., in classification problems), the proposed solution is easily adapted to obtain its final solution in a few iterations. An important advantage over the existing solutions is that these computations (for all neurons in a layer) are independent from each other; thus, they can be carried out in parallel to optimize all weights in a given layer simultaneously. Furthermore, its running time is deterministic in the sense that one can obtain the exact number of computations necessary to optimize the weights in all network layers (per iteration, in the case of non-injective mapping). Our simulation and empirical results show that the proposed scheme, BPLS, works well and is competitive with existing ones in terms of accuracy, but significantly surpasses them in terms of running time. To summarize, the new method is straightforward to implement, is competitive and computationally more efficient than the existing ones, and is well-tailored for parallel implementation.