Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive Vital Sign Monitoring via Facial Vibrations Leveraging AR/VR Headsets

Vital signs (e.g., breathing and heart rates) and personal identities are essential information for personalized medicine and healthcare. The popularity of augmented reality/virtual reality (AR/VR) provides an excellent opportunity for enabling long-term health monitoring in a broad range of scenarios, including virtual entertainment, education, and telemedicine. However, commercial-off-the-shelf AR/VR devices do not have dedicated biosensors for providing vital signs and personal identities. In this work, we propose a novel framework that can generate fine-grained vital sign signals and other personalized health information of an AR/VR user through passive sensing on AR/VR devices. In particular, we find that the user's minute facial vibrations induced by breathing and heart beating can impact the readily available motion sensors on AR/VR headsets, which encode rich vital sign patterns and unique biometrics. The proposed framework further estimates the breathing and heartbeat rates, detects the gender and identity, and derives the body fat percentage of the user. To mitigate the impacts of body movement, we design an adaptive filtering scheme to cancel the spontaneous and non-spontaneous motion artifacts. We also develop unique facial vibration features and deep learning techniques to facilitate vital sign signal reconstruction and user identification. Extensive experiments demonstrate that our framework can achieve a low error of vital sign signal reconstruction and rate measurement, along with 95.51% and 93.33% accuracy on identity and gender recognition.
ProMiSE: Process Mining Support for End-Users

In the past decade, process mining has gained momentum in academia and the industry, as it supports organizations in deriving insights from event data recorded from process executions. The increasing adoption of process mining in practice entails supporting process analysts in their work. Indeed, their analysis includes many exploratory tasks that require them to rely on their experience to interpret the data and steer the analysis. This knowledge-intensive nature of process mining can be challenging for less experienced analysts and calls for methodological and operational guidance tailored to their needs. In this paper, we present ProMiSE , a project funded by the Swiss National Science Foundation that embraces this novel direction in process mining research. The first goal of the project is to improve our understanding of how analysts work in practice, i
Formation and evolution of elliptical galaxies

We address the problem of the formation and evolution of elliptical galaxies (from dwarf to normal/giant systems). In particular, by means of N-body-tree–SPH simulations, incorporating cooling, star formation, energy feedback and chemical evolution, we intend to explore whether the formation of elliptical galaxies from the monolithic collapse of baryons inside non-rotating, virialized haloes of dark matter leads to results compatible with the main body of observational data on these systems. We show that this scenario can indeed reproduce the basic structural properties of observed elliptical galaxies of different mass. We study the star formation history and the chemical enrichment of the models, showing that the duration, strength and shape of the star formation rate as a function of time depend strongly on the galaxy mass and the initial density. More precisely, massive elliptical galaxies, independent of their initial density, show a single burst of star formation, whereas the low-mass ones have a varied star formation history. It is early and monolithic in the high initial density systems, and irregular and intermittent in the low-density ones. The occurrence of galactic winds is also analysed, and we conclude that in general all galaxies are able to eject part of their gas content into the intergalactic medium. However, the percentage of the ejected material increases with decreasing galaxy mass. Our models are used to interpret the fundamental plane (mass-to-light ratio versus mass relationship), the mass-to-light ratio versus central velocity dispersion, the colour–magnitude relation and the mass–radius relationships of elliptical galaxies from dwarfs to giants. For each relationship in question the agreement between observations and theory is remarkable. Based on this, we endeavour to speculate on a unified scheme for the formation of stellar aggregates going from globular clusters to normal/giant elliptical galaxies in which initial density, type of star formation (single initial episode or recurrent bursts of activity) and galactic winds are the key ingredients. Finally, we draw some conclusions concerning the long-standing question as to whether the hierarchical or the monolithic scheme for the formation of galaxies (ellipticals) ought to be preferred.
TEADAL: Trustworthy, Energy-Aware federated DAta Lakes along the computing continuum

While the value that data analytics has in any organization is undoubted and widely recognized, most approaches and solutions are mainly based on processing data that are internally produced or, if available from the outside, are publicly available with almost no restrictions. The situation becomes, however, more challenging when data analytics can take advantage of data owned by other organizations, which are only available to share under some conditions that must not affect the value that such data has for them. In this context, the need for data sharing has to deal with the need for data sovereignty, i.e., the possibility of an organization to exert full control over their data. This introduces barriers at both organizational and technological level: data must be shared under an agreement established among the parties which could affect the locations – on premise or on cloud – where the storage and processing of the data are performed. The goal of the TEADAL project is to propose and provide a set of tools to enable the creation of a federation of data lakes, particularly reducing the barriers in defining the terms under which data can be shared. The actual sharing shall optimize the exploitation of resources owned by the members to foster efficient, energy-aware, and trusted data management.
Quantifying Statistical Interdependence PART III: n > 2 Multi-Dimensional Point Processes

Stochastic event synchrony (SES) is a technique to quantify the similarity of pairs of signals. In this paper (Part III), SES is extended from pairs of signals to collections of signals. As in Part I and II, first “events” are extracted from the given time series. Next, one tries to align events from one time series with events from the other. The better the alignment, the more similar the collection of time series is considered to be. As in Part II, this paper deals with multi-dimensional events. Although the basic idea is similar to the pairwise case, the extension to collection of point processes involves an NP-hard combinatorial problem, and therefore, it is far from trivial.
Music360: Modeling the Value of Music

Music played in shops, bars, restaurants, and other venues contributes to their revenue, increases the well-being of people, and is an essential source of revenue for intellectual property rights (IPR) holders of the music. However, these venues that reproduce music lack information about the value the music provides to their business, music Organizations (CMOs) lack information for accurate royalty distribution, creators lack information about the usage of their music, and policymakers are in the dark about the social and economic value of music. We aim to create a model-driven platform that supports the transparent delivery of music usage and value provided to stakeholders in the EU music ecosystem, including, creators, venues, and policymakers. As a result, creators will be able to identify new business opportunities and have a clear vision of royalties distribution, venues will be able to assess the value of music for their revenue, and policymakers gain insight into the impact of different events in the music ecosystem.
Radiation mechanisms and physical properties of the γ-ray narrow-line Seyfert 1 galaxies

We investigate the physical properties and radiation mechanisms of 11 states of five narrow-line Seyfert 1 (NLS1) galaxies detected by the Large Area Telescope on board Fermi through modeling the quasi-simultaneous multi-band observations. We obtain the best-fitting model parameters and their uncertainties for each state with the chi(2)-minimization procedure and discuss their implications on the characteristics of jet. Similar to blazars, their spectral energy distributions (SEDs) have a two-humped structure and their non-thermal emission can be modelled with the single-zone synchrotron + inverse Compton (IC) model. For all states, the GeV gamma-rays may be contributed by the external Compton (EC) emission components. The observations of Fermi are mostly located at the declining stage of the EC humps. T-ext < 0.5 eV in all cases (T-ext is the characteristic temperature of external soft photons), suggesting that their radiation zones may be usually located outside of the broad line region (BLR) and the soft photons of Compton scattering mainly come from the dust torus. Compared with the bright Fermi blazars studied by Ghisellini et al. (2014, Nature, 515, 376), the Pjet (the power of the jets) of NLS1 galaxies detected by Fermi is similar to that of the flat spectrum radio quasars (FSRQs) but a little larger than that of the BL Lac objects (BL Lacs). However, a comparison of Pr (the powers of radiations) with the FSRQs and BL Lac objects shows that NLS1 galaxies' Pr has values comparable to BL Lac objects but lower than FSRQs in spite of having similar Pjet values and the same energy carrier (the cold protons) as the FSRQs. Observations indicate that gamma-NLS1 galaxies might have lower. (efficiency of gravitational energy release) values than GeV blazars.
COMPACT DUAL-BAND ANNULAR-RING SLOT ANTENNA WITH MEANDERED GROUNDED STRIP

COMPACT DUAL-BAND ANNULAR-RING SLOTANTENNA WITH MEANDERED GROUNDED STRIPJ. -Y. SzeDepartment of Electrical and Electronic EngineeringChung Cheng Institute of TechnologyNational Defense University190 Sanyuan 1st. St., Dasi Township, Taoyuan County 33508, Taiwan,R.O. C.T. -H. Hu and T. -J. ChenDepartment of Electrical EngineeringDayeh University168 University Rd., Dacun, Changhua County 51591, Taiwan, R.O. C.Abstract|A compact dual-band annular-ring slot antenna (ARSA) isproposed for use in 2.4/5GHz wireless local-area networks (WLANs). With a meandered grounded strip embedded in the ring slot, threeresonant modes were excited. With a pair of notches properly etchedin the inner circular patch, the third resonant band was su–cientlylowered so that the second and third resonant bands are combinedto form a wider upper operating band. If scaled to the same loweroperating-band center frequency, the proposed ARSA measures only53.6% the area of a conventional microstrip-line-fed ARSA. Measuredand simulated results were found to agree reasonably well with eachother.1. INTRODUCTIONDue to the advantages of large impedance bandwidths and low proﬂles,printed slot antennas (PSAs) have received much attention in recentyears. Some PSAs were designed to generate circularly polarizedwaves with desired handed senses [1{3]; some were further enhancedin their bandwidths [4{6]; others were developed to support dual-band operations [7{17]. For those microstrip-line-fed PSAs designedfor dual-band operations, many of them are designed to cover the
Policy Gradient Play with Networked Agents in Markov Potential Games

We introduce a distributed policy gradient play algorithm with networked agents playing Markov potential games. Agents have rewards at each stage of the game, that depend on the joint actions of agents given a common dynamic state. Agents implement parameterized and differentiable policies to take actions against each other. Markov potential games assume the existence of potential value functions. In a differentiable Markov potential game, partial gradients of a potential function are equal to the local gradients with respect to the individual parameters. In this work, agents receive information on other agents’ parameters via a communication network in addition to rewards. Agents then use stochastic gradients with respect to local estimates of joint policy parameters to update their policy parameters. We show that agents’ joint policy converges to a first-order stationary point of Markov potential value function with any type of function approximation, state and action spaces. Numerical experiments confirm the convergence result in the lake game, a Markov potential game.
PusH: Concurrent Probabilistic Programming with Function Spaces

We introduce a prototype probabilistic programming language (PPL) called Pus H for performing Bayesian inference on function spaces with a focus on Bayesian deep learning (BDL). We describe the core abstraction of Pus H based on particles that links models, specified as neural networks (NNs), with inference, specified as procedures on particles using a programming model inspired by message passing. Finally, we test Pus H on a variety of models and datasets used in scientific machine learning (SciML), a domain with natural function space inference problems, and we evaluate scaling of Pus H on single-node multi-GPU devices. Thus we explore the combination of probabilistic programming, NNs, and concurrency in the context of Bayesian inference on function spaces. The code can be found at https: //github.com/lbai-lab/PusH .
Sample Complexity Bound for Evaluating the Robust Observer's Performance under Coprime Factors Uncertainty

We address the end-to-end sample complexity bound for learning in closed loop the state estimatorbased robust H2 controller for an unknown (possibly unstable) Linear Time Invariant (LTI) system, when given a fixed state-feedback gain. We build on the results from Ding et al. (1994) to bridge the gap between the parameterization of all state-estimators and the celebrated Youla parameterization. Refitting the expression of the relevant closed loop allows for the optimal linear observer problem given a fixed state feedback gain to be recast as a convex problem in the Youla parameter. The robust synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant, such that a min-max optimization problem is formulated for the robust H2 controller via an observer approach. The closed-loop identification scheme follows Zhang et al. (2021), where the nominal model of the true plant is identified by constructing a Hankel-like matrix from a single time-series of noisy, finite length input-output data by using the ordinary least squares algorithm from Sarkar et al. (2020). Finally, a H∞ bound on the estimated model error is provided, as the robust synthesis procedure requires bounded additive uncertainty on the coprime factors of the model. Reference Zhang et al. (2022b) is the extended version of this paper.
Modelling daily value-at-risk using realized volatility, non-linear support vector machine and ARCH type models

The aim of this paper is to compare the performance of the daily nonlinear support vector machines, the new semi-parametric tool for regression estimation, heterogeneous autoregressive (SVM-HAR)ARCH type models based on the daily realized volatility (which uses intraday returns) with the performance of the classical HAR-ARCH type models by using different innovation distribution when the one-day ahead value-at-risk (VaR) is to be computed. The daily realized volatility is calculated using 5-, 15-min and optimally sampled intraday returns for Nikkei 225 index. This paper shows that the particular hybrid SVM-HAR-ARCH type model provides better performance when 15-min intraday returns are used. This paper also shows that the models based on a long memory skewed student distribution provide the better performance of one-day ahead value-at-risk forecasts.
Evaluación de microalgas para la producción de biomasa económicamente útil usando aguas producidas

La produccion de microalgas para la obtencion de biodiesel y otros productos de valor es un tema al que se le dedican grandes esfuerzos. Sin embargo, la produccion de microalgas requiere grandes volumenes de agua y para que su produccion sea sustentable se requiere de buscar otras fuentes de agua que no compitan con la produccion de alimentos. En este sentido las aguas producidas generadas por la industria petrolera podrian ser una fuente de agua alternativa por lo que para el presente estudio se evaluo la adaptacion y el crecimiento de tres cepas de microalgas halotolerantes en dos fuentes de agua producida de la cuenca de Sabinas al noreste de Mexico. Se encontro que una de las fuentes de agua producida, de la estacion Monclova 1, tiene potencial para su aprovechamiento utilizando la microalga Nannochloropsis sp. que presento mayor adaptabilidad a esta agua siendo una buena candidata para futuras investigaciones. Por otra parte, I. galbana no tolero la presencia de agua producida en el medio; sin embargo, al resembrar los tratamientos presento una respuesta de crecimiento poblacional positiva, lo que evidencio la capacidad de bioremediacion de esta cepa.
Conception et validation d'un régénérateur de suites binaires cryptographiquement sûres base sur les algorithmes évolutionnistes

CONCEPTION AND VALIDATION OF A REGENERATOR OF BINARY CRYPTOGRAPHICALLY SAFE SEQUENCES BASED ON EVOLUTIONIST ALGORITHMS The aim of this thesis is the conception and the validation of a new method of regeneration of binary cryptographically safe sequences. Thus the R.A NMJ algorithm that we present is a regenerator of binary cryptographically safe sequences, which is largely inspired by evolutionist algorithms, indeed the genetic algorithms. The R.A NMJ algorithm is based on new functions adapted to its objectives, from where we introduce the functions and classes of three states functions, that allow non linear and dissipative with compensation transforms. We give in this thesis a description of the three processes of the R.A NMJ algorithm, followed by a theoretical study of functions and classes of three states functions, these functions are largely exploited in this new approach, which allow an estimate of individual and collective complexities of theses functions. We will also present the results of the analysis of the chaotic behaviour of this algorithm through the analysis of its sensitivity towards initial conditions and disturbances in the vicinity of his attractors. At last, an analysis of the regenerated binary sequences is presented.
Beyond Biodiversity: Eliciting Diverse Values of Urban Green Spaces in Flanders

Nature-based solutions are claimed to offer an effective approach to tackle societal challenges and promote biodiversity. While research has mainly focused on biodiversity and material ecosystem services, non-material contributions and relational values of urban green spaces remain underexplored. How to balance the benefits of nature, well-being, and relational values in their design and performance evaluation remains unclear. To elicit the values expressed in public communication regarding the benefits of urban nature projects in Flanders, three online repositories that feature diverse nature-based solutions projects in the region were chosen. Using coding and quantitative content analysis of standardized descriptions from these repositories, this study found that relational values were most abundant (55%), followed by instrumental values (30%) and intrinsic values (15%), consistently so over socio-demographic and physical contexts. It was also discovered that larger projects have a higher level of multifunctionality, which is calculated based on the variety of values and value dimensions expressed, and that participation—although considered key—rarely reports on inclusivity. The findings suggest that in Flanders, a greater emphasis is placed on relational values associated with urban nature. A broader value scope for the design, management, and evaluation of urban green spaces tailored to the local context is recommended.
Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects

Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament. Algorithms from the family of random forests (an ensemble of decision trees) have become a powerful tool that can be used to classify astronomical events and objects. This work aims to illustrate the versatility of machine learning algorithms, such as decision trees, to facilitate the identification and classification of celestial bodies by manipulating hyperparameters and studying the attributes of celestial body datasets. By applying a random forest algorithm to a well-known dataset that includes three types of celestial bodies, its effectiveness was compared against some supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, and neural networks). The results show that random forests are a good alternative for data analysis and classification in astronomical observations.