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The stability and reproducibility issues related to metal halide Perovskite Solar Cells (PSCs) have prevented so far from exploiting this highly efficient photovoltaic technology under rainy or even moisture conditions and in combination with drop triboelectric nanogenerators (D-TENGs). Protecting the PSCs with a waterproof encapsulator could overcome such a disadvantage. Herein, we propose the implementation of fluorinated carbon (CFx) coatings produced by PECVD, enabling waterproof capability for the PSC and working as an energy harvesting surface for D-TENGs. These 100 nm - films present optimal optical transmittance, fully preserving the photovoltaic parameters of the cell. The improved long-term stability of the water-resistant PSCs prevents degradation under illumination in outdoor or simulated adverse environments with high humidity, high temperature, water immersion, or rain. As a remarkable result, the 50% Power Conversion Efficiency (PCE) has been retained after ten days of illumination under 100% relative humidity at 50 degrees Celsius. Also, the CFx coatings were successfully tested as a promoter agent to integrate commercial UV-curable sealants compatible with Spiro-OMeTAD, enhancing the performance stability of up to 80 % of PCE after 100 hours under illumination in a humid environment. This PSC was tested in a top-bottom electrode configuration for harvesting kinetic energy from droplets. Devices were compatible with simultaneously working as D-TENG and photovoltaic cells, yielding voltage outputs up to 12 V with maximum peak power density reaching 2.75 uW/cm2 defined by the D-TENG and PCE of 11.5 % and 8.46 mA/cm2 of short circuit current determined by the PSC under dripping and for an illumination angle of 45 degrees. The durability of the multisource device was tested under constant illumination and periodical drop impacting for more than 5 hours.

We consider a population growth model given by a two-type continuous-state branching process with immigration and competition, introduced by Ma. We study the relative frequency of one of the types in the population when the total mass is forced to be constant at a dense set of times. The resulting process is described as the solution to an SDE, which we call the culled frequency process, generalizing the $\Lambda$-asymmetric frequency process introduced by Caballero et al. We obtain conditions for the culled frequency process to have a moment dual and show that it is given by a branching-coalescing continuous-time Markov chain that describes the genealogy of the two-type CBI with competition. Finally, we obtain a large population limit of the culled frequency process, resulting in a deterministic ordinary differential equation (ODE). Two particular cases of the limiting ODE are studied to determine if general two-type branching mechanisms and general Malthusians can lead to the coexistence of the two types in the population.

Detecting salient parts in text using natural language processing has been widely used to mitigate the effects of information overflow. Nevertheless, most of the datasets available for this task are derived mainly from academic publications. We introduce SPACE-IDEAS, a dataset for salient information detection from innovation ideas related to the Space domain. The text in SPACE-IDEAS varies greatly and includes informal, technical, academic and business-oriented writing styles. In addition to a manually annotated dataset we release an extended version that is annotated using a large generative language model. We train different sentence and sequential sentence classifiers, and show that the automatically annotated dataset can be leveraged using multitask learning to train better classifiers., Comment: Accepted in LREC-COLING 2024

We study the optimal bailout dividend problem with transaction costs for an insurance company, where shareholder payouts align with the arrival times of an independent Poisson process. In this scenario, the underlying risk model follows a spectrally negative L\'evy process. Our analysis confirms the optimality of a periodic $(b_{1},b_{2})$-barrier policy with classical reflection at zero. This strategy involves reducing the surplus to $b_1$ when it exceeds $b_{2}$ at the Poisson arrival times and pushes the surplus to 0 whenever it goes below zero.

Background: Eating disorders are increasingly prevalent, and social networks offer valuable information. Objective: Our goal was to identify efficient machine learning models for categorizing tweets related to eating disorders. Methods: Over three months, we collected tweets about eating disorders. A 2,000-tweet subset was labeled for: (1) being written by individuals with eating disorders, (2) promoting eating disorders, (3) informativeness, and (4) scientific content. Both traditional machine learning and deep learning models were employed for classification, assessing accuracy, F1 score, and computational time. Results: From 1,058,957 collected tweets, transformer-based bidirectional encoder representations achieved the highest F1 scores (71.1%-86.4%) across all four categories. Conclusions: Transformer-based models outperform traditional techniques in classifying eating disorder-related tweets, though they require more computational resources.

Parkinson's disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson's severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson's disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson's Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson's disease or non-severe Parkinson's disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson's outperforming the state-of-the-art proposals.

Accurate simulation techniques are indispensable to efficiently propose new memory or architectural organizations. As implementing new hardware concepts in real systems is often not feasible, cycle-accurate simulators employed together with certain benchmarks are commonly used. However, detailed simulators may take too much time to execute these programs until completion. Therefore, several techniques aimed at reducing this time are usually employed. These schemes select fragments of the source code considered as representative of the entire application's behaviour -- mainly in terms of performance, but not plenty considering the behaviour of cache memory levels -- and only these intervals are simulated. Our hypothesis is that the different simulation windows currently employed when evaluating microarchitectural proposals, especially those involving the last level cache (LLC), do not reproduce the overall cache behaviour during the entire execution, potentially leading to wrong conclusions on the real performance of the proposals assessed. In this work, we first demonstrate this hypothesis by evaluating different cache replacement policies using various typical simulation approaches. Consequently, we also propose a simulation strategy, based on the applications' LLC activity, which mimics the overall behaviour of the cache much closer than conventional simulation intervals. Our proposal allows a fairer comparison between cache-related approaches as it reports, on average, a number of changes in the relative order among the policies assessed -- with respect to the full simulation -- more than 30\% lower than that of conventional strategies, maintaining the simulation time largely unchanged and without losing accuracy on performance terms, especially for memory-intensive applications.

Knowledge base population seeks to expand knowledge graphs with facts that are typically extracted from a text corpus. Recently, language models pretrained on large corpora have been shown to contain factual knowledge that can be retrieved using cloze-style strategies. Such approach enables zero-shot recall of facts, showing competitive results in object prediction compared to supervised baselines. However, prompt-based fact retrieval can be brittle and heavily depend on the prompts and context used, which may produce results that are unintended or hallucinatory.We propose to use textual entailment to validate facts extracted from language models through cloze statements. Our results show that triple validation based on textual entailment improves language model predictions in different training regimes. Furthermore, we show that entailment-based triple validation is also effective to validate candidate facts extracted from other sources including existing knowledge graphs and text passages where named entities are recognized., Comment: Accepted to ISWC'23 - The International Semantic Web Conference

Preventing the spread of misinformation is challenging. The detection of misleading content presents a significant hurdle due to its extreme linguistic and domain variability. Content-based models have managed to identify deceptive language by learning representations from textual data such as social media posts and web articles. However, aggregating representative samples of this heterogeneous phenomenon and implementing effective real-world applications is still elusive. Based on analytical work on the language of misinformation, this paper analyzes the linguistic attributes that characterize this phenomenon and how representative of such features some of the most popular misinformation datasets are. We demonstrate that the appropriate use of pertinent symbolic knowledge in combination with neural language models is helpful in detecting misleading content. Our results achieve state-of-the-art performance in misinformation datasets across the board, showing that our approach offers a valid and robust alternative to multi-task transfer learning without requiring any additional training data. Furthermore, our results show evidence that structured knowledge can provide the extra boost required to address a complex and unpredictable real-world problem like misinformation detection, not only in terms of accuracy but also time efficiency and resource utilization., Comment: Accepted at K-CAP'23: The 12th Knowledge Capture Conference

The Committee on Radio Astronomy Frequencies (CRAF) is an Expert Committee of the European Science Foundation. It aims to provide a cost-effective single voice on frequency protection issues for European radio astronomy observatories and research institutes, achieving a significantly greater impact than that achievable by individual national institutions. By working together, European observatories and institutes can profit from synergy effects, cover many more topics, and learn from each other. CRAF was founded in 1988 and has since then been engaged with the International Telecommunication Union (ITU), in particular its Radiocommunication Sector (ITU-R), and the European Conference of Postal and Telecommunications Administrations (CEPT) and its European Communications Committee (ECC). This is the self-evaluation report prepared by CRAF for its periodic review of the years 2011-2021., Comment: 75 pages

Using a reactive molecular beam with high kinetic energy ($E_{kin}$) it is possible to speed gas-surface reactions involving high activation barriers ($E_{act}$), which would require elevated pressures ($P_0$) if a random gas with a Maxwell-Boltzmann distribution is used. By simply computing the number of molecules that overcome the activation barrier in a random gas at $P_0$ and in a molecular beam at $E_{kin}$=$E_{act}$, we establish an $E_{kin}$-$P_0$ equivalence curve, through which we postulate that molecular beams are ideal tools to investigate gas-surface reactions that involve high activation energies. In particular, we foresee the use of molecular beams to simulate gas surface reactions within the industrial-range ($>$ 10 bar) using surface-sensitive Ultra-High Vacuum (UHV) techniques, such as X-ray photoemission spectroscopy (XPS). To test this idea, we revisit the oxidation of the Cu(111) surface combining O$_2$ molecular beams and XPS experiments. By tuning the kinetic energy of the O$_2$ beam in the range 0.24-1 eV we achieve the same sequence of surface oxides obtained in Ambient Pressure Photoemission (AP-XPS) experiments, in which the Cu(111) surface was exposed to a random O$_2$ gas up to 1 mbar. We observe the same surface oxidation kinetics as in the random gas, but with a much lower dose, close to the expected value derived from the equivalence curve.

We revisit an absolutely-continuous version of the stochastic control problem driven by a L\'evy process. A strategy must be absolutely continuous with respect to the Lebesgue measure and the running cost function is assumed to be convex. We show the optimality of a refraction strategy, which adjusts the drift of the state process at a constant rate whenever it surpasses a certain threshold. The optimality holds for a general L\'evy process, generalizing the spectrally negative case presented in Hern\'andez-Hern\'andez et al.(2016)., Comment: 24 pages

We consider a singular control problem that aims to maximize the expected cumulative rewards, where the instantaneous returns depend on the state of a controlled process. The contributions of this paper are twofold. Firstly, to establish sufficient conditions for determining the optimality of the one-barrier strategy when the uncontrolled process $X$ follows a spectrally negative L\'evy process with a L\'evy measure defined by a completely monotone density. Secondly, to verify the optimality of the $(2n+1)$-barrier strategy when $X$ is a Brownian motion with a drift. Additionally, we provide an algorithm to compute the barrier values in the latter case.

This paper studies a general L\'evy process model of the bail-out optimal dividend problem with an exponential time horizon, and further extends it to the regime-switching model. We first show the optimality of a double barrier strategy in the single-regime setting with a concave terminal payoff function. This is then applied to show the optimality of a Markov-modulated double barrier strategy in the regime-switching model via contraction mapping arguments. We solve these for a general L\'evy model with both positive and negative jumps, greatly generalizing the existing results on spectrally one-sided models.

Motivated by the question of the impact of selective advantage in populations with skewed reproduction mechanims, we study a Moran model with selection. We assume that there are two types of individuals, where the reproductive success of one type is larger than the other. The higher reproductive success may stem from either more frequent reproduction, or from larger numbers of offspring, and is encoded in a measure $\Lambda$ for each of the two types. Our approach consists of constructing a $\Lambda$-asymmetric Moran model in which individuals of the two populations compete, rather than considering a Moran model for each population. Under certain conditions, that we call the "partial order of adaptation", we can couple these measures. This allows us to construct the central object of this paper, the $\Lambda-$asymmetric ancestral selection graph, leading to a pathwise duality of the forward in time $\Lambda$-asymmetric Moran model with its ancestral process. Interestingly, the construction also provides a connection to the theory of optimal transport. We apply the ancestral selection graph in order to obtain scaling limits of the forward and backward processes, and note that the frequency process converges to the solution of an SDE with discontinous paths. Finally, we derive a Griffiths representation for the generator of the SDE and use it to find a semi-explicit formula for the probability of fixation of the less beneficial of the two types., Comment: Substantially revised. The results are unchanged with respect to v1, but the presentation has been changed. References added and corrected

We present the design, fabrication and discuss the performance of a new combined high-resolution Scanning Tunneling and thermopower Microscope (STM/SThEM). We also describe the development of the electronic control, the user interface, the vacuum system, and arrangements to reduce acoustical noise and vibrations. We demonstrate the microscope performance with atomic-resolution topographic images of Highly oriented pyrolytic graphite (HOPG) and local thermopower measurements in the semimetal Bi2Te3 sample. Our system offers a tool to investigate the relationship between electronic structure and thermoelectric properties at the nanoscale., Comment: 11 pages, 9 figures

We consider a version of the continuous-time multi-armed bandit problem where decision opportunities arrive at Poisson arrival times, and study its Gittins index policy. When driven by spectrally one-sided L\'evy processes, the Gittins index can be written explicitly in terms of the scale function, and is shown to converge to that in the classical L\'evy bandit of Kaspi and Mandelbaum (1995).

This work aims to improve fuel chamber injectors' performance in turbofan engines, thus implying improved performance and reduction of pollutants. This requires the development of models that allow real-time prediction and improvement of the fuel/air mixture. However, the work carried out to date involves using experimental data (complicated to measure) or the numerical resolution of the complete problem (computationally prohibitive). The latter involves the resolution of a system of partial differential equations (PDE). These problems make difficult to develop a real-time prediction tool. Therefore, in this work, we propose using machine learning in conjunction with (complementarily cheaper) single-phase flow numerical simulations in the presence of tangential discontinuities to estimate the mixing process in two-phase flows. In this meaning we study the application of two proposed neural network (NN) models as PDE surrogate models. Where the future dynamics is predicted by the NN, given some preliminary information. We show the low computational cost required by these models, both in their training and inference phases. We also show how NN training can be improved by reducing data complexity through a modal decomposition technique called higher order dynamic mode decomposition (HODMD), which identifies the main structures inside flow dynamics and reconstructs the original flow using only these main structures. This reconstruction has the same number of samples and spatial dimension as the original flow, but with a less complex dynamics and preserving its main features. The core idea of this work is to test the limits of applicability of deep learning models to data forecasting in complex fluid dynamics problems. Generalization capabilities of the models are demonstrated by using the same NN architectures to forecast the future dynamics of four different two-phase flows., Comment: 48 pages, 21 figures, 13 tables. Submitted to Expert Systems with Applications

The main purpose of this paper is to study time-inhomogeneous one-dimensional branching processes (mainly on a continuous but also on a discrete state space) with the help of recent achievements in Loewner Theory dealing with evolution families of holomorphic self-maps in simply connected domains of the complex plane. Under a suitable stochastic continuity condition, we show that the families of the Laplace exponents of branching processes on $[0,\infty]$ can be characterized as topological (i.e. depending continuously on the time parameters) reverse evolution families whose elements are Bernstein functions. For the case of a stronger regularity w.r.t. time, we establish a Loewner - Kufarev type ODE for the Laplace exponents and characterize branching processes with finite mean in terms of the vector field driving this ODE. Similar results are obtained for families of probability generating functions for branching processes on the discrete state space $\{0,1,2,\ldots\}\cup\{\infty\}$. In addition, we find necessary and sufficient conditions for "spatial" embeddability of such branching processes into branching processes on $[0,\infty]$. Finally, we give some probabilistic interpretations of the Denjoy - Wolff point at $0$ and at $\infty$., Comment: Reference [16] has been updated

Inspired by recent and revolutionary developments in AI, particularly in language understanding and generation, we set about designing AI systems that are able to address complex scientific tasks that challenge human capabilities to make new discoveries. Central to our approach is the notion of natural language as core representation, reasoning, and exchange format between scientific AI and human scientists. In this paper, we identify and discuss some of the main research challenges to accomplish such vision.